{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from augerino import datasets, models\n",
    "import numpy as np\n",
    "from torch.utils.data import DataLoader\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import torch.nn.functional as F\n",
    "from augerino.models.e2_steerable import C8SteerableCNN, SmallE2\n",
    "from smallnet import smallnet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "use_cuda = torch.cuda.is_available()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpt_path = \"./saved-outputs/\"\n",
    "ncopies = 12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "net = smallnet(in_channels=1,num_targets=10)\n",
    "augerino = models.UniformAug()\n",
    "low_model = models.AugAveragedModel(net, augerino,ncopies=ncopies)\n",
    "\n",
    "net = smallnet(in_channels=1,num_targets=10)\n",
    "augerino = models.UniformAug()\n",
    "mid_model = models.AugAveragedModel(net, augerino,ncopies=ncopies)\n",
    "\n",
    "net = smallnet(in_channels=1,num_targets=10)\n",
    "augerino = models.UniformAug()\n",
    "high_model = models.AugAveragedModel(net, augerino,ncopies=ncopies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "low_model.load_state_dict(torch.load(checkpt_path + \"model0.01.pt\"))\n",
    "mid_model.load_state_dict(torch.load(checkpt_path + \"model0.05.pt\"))\n",
    "high_model.load_state_dict(torch.load(checkpt_path + \"model0.1.pt\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net = smallnet(in_channels=1,num_targets=10)\n",
    "augerino = models.UniformAug()\n",
    "init_model = models.AugAveragedModel(net, augerino,ncopies=ncopies)\n",
    "init_model.load_state_dict(torch.load(checkpt_path + \"model0.01_init.pt\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/greg_b/e2cnn/e2cnn/nn/modules/r2_conv/basisexpansion_singleblock.py:61: UserWarning: indexing with dtype torch.uint8 is now deprecated, please use a dtype torch.bool instead. (Triggered internally at  /pytorch/aten/src/ATen/native/IndexingUtils.h:20.)\n",
      "  sampled_basis = sampled_basis[mask, ...]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e2_model = SmallE2(channel_in=1, n_classes=10, rot_n=10)\n",
    "e2_model.load_state_dict(torch.load(\"./saved-outputs/e2_epoch200.pt\", map_location='cpu'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/greg_b/miniconda3/envs/tf-gpu/lib/python3.8/site-packages/torch/nn/functional.py:3447: UserWarning: Default grid_sample and affine_grid behavior has changed to align_corners=False since 1.3.0. Please specify align_corners=True if the old behavior is desired. See the documentation of grid_sample for details.\n",
      "  warnings.warn(\"Default grid_sample and affine_grid behavior has changed \"\n",
      "/home/greg_b/miniconda3/envs/tf-gpu/lib/python3.8/site-packages/torch/nn/functional.py:3384: UserWarning: Default grid_sample and affine_grid behavior has changed to align_corners=False since 1.3.0. Please specify align_corners=True if the old behavior is desired. See the documentation of grid_sample for details.\n",
      "  warnings.warn(\"Default grid_sample and affine_grid behavior has changed \"\n"
     ]
    }
   ],
   "source": [
    "dataset = datasets.RotMNIST(\"~/datasets/\", train=True)\n",
    "trainloader = DataLoader(dataset, batch_size=128)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "img, lab = next(iter(trainloader))\n",
    "init_aug = init_model.aug(img)\n",
    "end_aug = mid_model.aug(img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's Mess Around With Rotating Inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = datasets.RotMNIST(\"~/datasets/\", train=False)\n",
    "testloader = DataLoader(dataset, batch_size=128)\n",
    "testimg, testlab =next(iter(trainloader))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([  2,   9,  20,  26,  53,  58,  60,  61,  64,  89,  92, 115, 127]),)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.where(testlab==4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "ind = 64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/greg_b/miniconda3/envs/tf-gpu/lib/python3.8/site-packages/torch/nn/functional.py:3447: UserWarning: Default grid_sample and affine_grid behavior has changed to align_corners=False since 1.3.0. Please specify align_corners=True if the old behavior is desired. See the documentation of grid_sample for details.\n",
      "  warnings.warn(\"Default grid_sample and affine_grid behavior has changed \"\n",
      "/home/greg_b/miniconda3/envs/tf-gpu/lib/python3.8/site-packages/torch/nn/functional.py:3384: UserWarning: Default grid_sample and affine_grid behavior has changed to align_corners=False since 1.3.0. Please specify align_corners=True if the old behavior is desired. See the documentation of grid_sample for details.\n",
      "  warnings.warn(\"Default grid_sample and affine_grid behavior has changed \"\n"
     ]
    }
   ],
   "source": [
    "n_ang = 50\n",
    "angles = torch.linspace(-np.pi, np.pi, n_ang)\n",
    "\n",
    "four = testimg[ind, ::].unsqueeze(0)\n",
    "batch_four = torch.cat(n_ang * [four])\n",
    "\n",
    "\n",
    "with torch.no_grad():\n",
    "    # Build affine matrices for random translation of each image\n",
    "    affineMatrices = torch.zeros(n_ang,2,3)\n",
    "    affineMatrices[:,0,0] = angles.cos()\n",
    "    affineMatrices[:,1,1] = angles.cos()\n",
    "    affineMatrices[:,0,1] = angles.sin()\n",
    "    affineMatrices[:,1,0] = -angles.sin()\n",
    "\n",
    "    flowgrid = F.affine_grid(affineMatrices, size = batch_four.size())\n",
    "    \n",
    "    rot_four = F.grid_sample(batch_four, flowgrid)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "low_model.eval();\n",
    "high_model.eval();\n",
    "mid_model.eval();\n",
    "e2_model.eval();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# low_model.train();\n",
    "# high_model.train();\n",
    "# mid_model.train();\n",
    "# e2_model.train();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "with torch.no_grad():\n",
    "    low_preds = low_model(rot_four)\n",
    "    high_preds = high_model(rot_four)\n",
    "    mid_preds = mid_model(rot_four)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "with torch.no_grad():\n",
    "    e2_preds = e2_model(rot_four)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "sftmx = torch.nn.Softmax(-1)\n",
    "low_probs = sftmx(low_preds)\n",
    "high_probs = sftmx(high_preds)\n",
    "mid_probs = sftmx(mid_preds)\n",
    "e2_probs = sftmx(e2_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "tick_pts = [-np.pi, -np.pi/2, 0, np.pi/2, np.pi]\n",
    "tick_labs = [r\"-$\\pi$\", r'-$\\pi$/2', '0', r'$\\pi$/2', r'$\\pi$']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### distribution plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "softplus = torch.nn.Softplus()\n",
    "test_pts = torch.linspace(-2, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_density(test_pts, width):\n",
    "    dist = torch.distributions.Uniform(-width/2., width/2.)\n",
    "    dens = dist.log_prob(test_pts).exp().detach()\n",
    "    return dens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# init_dens = get_density(test_pts, softplus(torch.tensor(-1.)))\n",
    "# high_dens = get_density(test_pts, high_logger[500:501]['width2'].values[0])\n",
    "# mid_dens = get_density(test_pts, mid_logger[500:501]['width2'].values[0])\n",
    "# low_dens = get_density(test_pts, low_logger[500:501]['width2'].values[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "init_dens = get_density(test_pts, softplus(torch.tensor(-1.)))\n",
    "width = min(softplus(high_model.aug.width[2]), 2*np.pi)\n",
    "high_dens = get_density(test_pts, width)\n",
    "width = min(softplus(mid_model.aug.width[2]), 2*np.pi)\n",
    "mid_dens = get_density(test_pts, width)\n",
    "width = min(softplus(low_model.aug.width[2]), 2*np.pi)\n",
    "low_dens = get_density(test_pts, width)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot it all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_img(fig, img, i, j, label):\n",
    "    ax = plt.Subplot(fig, inner[i,j])\n",
    "    ax.imshow(img, cmap=\"Greys\", interpolation='nearest')\n",
    "    ax.set_title(label, fontsize=ax_fs+4, y=-0.5)\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([])\n",
    "    fig.add_subplot(ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AvGvXLj344IPav3+/6bypqakaP3681q1bp5dffrnYf++yOHXqlJ566iktX77cVHxubq6+++47LVmyRAMHDtRjjz2m8PBwt9dVWgcPHtT48eO1fv36Ui2/c+dOjRkzRpdffrleeumlEhtfSpKcnKyHH35Yv/76a6mWPy8vL09r1qzRmjVr1LNnT5/+rgUAAAAQ2CIjI4vctGHmIQDSuacfTpkyRR9++KGp8zPp6elatmyZli1bpu7du+s///mPateuXaq63eGPP/7QqFGjdODAAZeWS0lJ0fz58/XVV1/pxhtv1IQJExQaGuqZIstJSkqKnn32WS1evNjUgzD++usvff755/rqq6906623aty4cW75DHJycjRx4kTNmzfPaezevXv16KOP6uuvv9bbb7/NyI0AAAAAfE5Zjrkljrs57ua4OzMzUy+++KLmzZtn6mGxqamp+t///qeFCxfqX//6l55++mnFxsaWQ6W+7+OPP9aLL76o7OzsEuNOnDiht99+W/Pnz9eMGTPUsGHDMq/7zz//1KhRo/T777+XGJeVlaUlS5Zo+fLluuuuu/Tggw8yQggAlBHfogC8buDAgQ7nG4ahlStXms7j6oGlM7/++qtuvPFG7dy506153enYsWNlbpK4UFpamkaNGqX//ve/bstZWpUrV3Y439Wn6HuSu//PLVu2TLfeemuJT8AorZycHA0fPtylJokLHTlyRMOGDVNycrLd/M2bN2vQoEEuNUlcaOHChXryySdLtWxJkpOTdeutt5pukriQYRj65JNPdPvtt/tMY86KFSvUv3//UjdJXGj16tXq37+/qRFFCjt06JAGDRpU5iYJAAAAAPAlaWlpReaZeYhBZmamRo8erXfffbdU52d++OEH3XLLLaUeqc8dTpw4UabzG4ZhaN68ebr99ttdutHF1xw6dEgDBw7UokWLXB4tNDc3V3PmzNGdd95ZqpFeL5Sdna3hw4ebulnjQqtXr9aIESNKHEkTAAAAALyhtMfcEsfdEsfdUsU+7k5JSdHQoUP16aefmmqSuJBhGPrmm2902223FbnPoyJ67bXXNHHiRKdNEhc6fPiwbrvtNh07dqxM6z7//99Zk8SF8vLyNHXqVD3++OPFjngCADCHESUAeF1CQoLq16+vP//8s8hrq1ev1s0331zmdcTFxalhw4aKjo5WpUqVZLFYdObMGR09elR//PFHsT8qjx49qnvvvVcLFiwo9sZ9XxUSEqJGjRqpcuXKqlSpksLDw3X27FmlpqZqz549Sk9Pd7hcQUGBXn31VdWtW1fXXHNNOVf9t1q1ajmc/+2332rMmDE+//T5atWqqW7duoqJiVGlSpWUl5ens2fPKikpSQcPHiz24P/333/XmDFj9Nlnn7n1iRCPP/54kWEJJalx48aqXbu24uLidObMGe3fv1+HDh1ymOPEiRN65JFH9Mknn0g6N8LBiBEjipzcCgsLU4sWLVS1alWFh4frxIkT2rZtW7H/5xYsWKDevXurb9++ZXyX56Slpenuu+8ucyPLtm3bdOedd+rjjz/22CgfZixcuFDjxo0r8cRHdHS0mjVrpri4OEVGRio1NVVJSUnFfgZHjx7VkCFD9OWXX6pGjRqm6sjLy9N9991nN0xkYZGRkWrYsKFq1qypiIgIWSwWnT17VmlpaTp48KBOnDhhal0AAAAAUF7+/PNPhzdb1KtXr8TlCgoKNGrUKP38889FXouKilLPnj2VkJCgatWq6ezZszpw4ICWLVumgwcP2sX+9ddfGjRokL788ks1aNDA4bpq1aqlFi1aSJJOnz6tI0eO2L3epEkThYSElFhv1apVS3z9vNjYWLVt21aNGzdW/fr1FR0drcjISGVlZenUqVPau3ev1qxZU+QC+2+//aannnpKb775ZrG5IyMjbe9DUpEbVapWreq0zurVq5t6H644efKkBg4cqOPHjxd5rWbNmurTp4+aNGmi2NhYpaSkaNeuXVq6dKlSU1PtYjdu3Kg777xTc+fOLfU5nSeffNLuIQmNGjVSt27d1LhxY8XGxurs2bPasWOHvv/++yLH2Bs2bNDMmTN11113lWrdAAAAAOBupT3mljju5rib4+6srCwNHTpUe/bsKfJa5cqV1adPHzVv3lzx8fFKTU3Vvn37tGTJEv311192sXv37tVtt92mBQsWKC4uzuG6Lvx/I53bdjMyMmzTkZGRql+/frG1+voIl3PnztXUqVNt0/Hx8erWrZvatm2r+Ph4ZWdn6+DBg1q2bFmRZoaTJ09qwoQJdsu7Ijc3V2PGjLH7/9SwYUP17dtX9evXV2RkpI4fP65Nmzbphx9+KNLI8fXXX6tSpUp6+umnS7V+TyvP708AKC0aJQD4hMsuu8xho8TWrVtLla9KlSrq1auXevToodatWxd7070kZWRk6Mcff9Qnn3zi8GntR48e1RNPPKH333/f6XpXrFhhN92rV68iNxX369dPkydPNvlOzAsJCdEll1yiK664Qh06dCjxx2ZBQYF27typL7/8Ul9++aWysrKKxIwfP15t27Y1dZLCE7p06eLwM8/IyNB9992n//73vz41PGDt2rXVu3dvdevWTa1bt1aVKlWKjT1z5oyWLl2qjz/+WNu3by/y+o4dO/Tiiy9q/Pjxbqntq6++smuSiIyM1F133aX+/fs7HGZ0165deumll7RmzZoir23atEkLFy5Unz59dN999+nMmTO215o1a6b77rtP3bp1U0REhN1yOTk5Wrx4sV5++WWHJ0Cef/55de/eXWFhYWV5q5Kk5557Tnv37rWb17ZtW1133XW65JJLVKNGDYWHh+uvv/7Snj17tHjxYn3//ffKyckpkmvXrl1OTzh50oYNG/TYY485bJIICQnR9ddfr1tuuUWtW7eW1WotEnPo0CHNnTtXc+bMKXJAffz4cY0bN04ffvihqaEaP/74Y4dPW7FYLPrnP/+pgQMHqn379goOLv7nZUpKirZu3aoVK1Zo1apVOnr0qNP1AgAAAIAnLVmypMi82NhYNWnSpMTlpk+f7vBmjZtvvlnjxo1TTExMkdfGjRunL7/8UpMnT7Y7nk5LS9PDDz+sTz/91OG5nAceeEAPPPCAJGn+/Pl64okn7F7/73//q7p165ZYb0mqVaumfv36qU+fPmrTpo3TY0TDMPTjjz9q0qRJdg363333nb777jtdddVVDpdr27atvv76a9t08+bN7V6/9dZbNWbMmFK/j9IwDEOPP/54kXMV4eHheuihhzR48GCHx9tPP/203nrrLX344Yd2D2DZvn27XnnllVKNnrllyxbbsXu1atX09NNPF/tZPvLII5o4caIWLFhgN//999/X7bffrvDwcJfXDwAAAADuVtpjbonjbo67Oe6eNGlSkSYJq9Wqu+++W6NGjXJ4b8UTTzyhDz/8UG+++abd/Q9HjhzR008/rSlTpjhc14X/byRp8ODBdveYtGnTRnPmzCnL2/GqSZMmSTp3j8X999+voUOHOvz8HnjgAX300UeaNGmS3f+7VatWKTExUe3bt3d53UuXLrX9v6tUqZKefPJJ9evXr0jcHXfcoWPHjunf//63Vq5caffaRx99pD59+uiSSy5xef2e5unvTwBwB+d3xQFAOUhISHA4Pzk5WSkpKaZyWCwWXXLJJZo2bZpWr16t5557TldccUWJTRLSuZvGr7rqKs2ePVuvvvqqoqKiisSsXLnS4dP4fUGNGjX02GOP6eeff9YHH3yggQMHqkWLFiV25AYFBal169aaMGGCvvnmG4c/5s+ePat3333Xg5WX7KKLLiq2ESIxMVHXXHONPvnkE7su9vIWFBSkK664Qp9++qlWrlypp59+Wt26dSuxSUI6d/AzYMAAffHFF3rqqacc3lj+ySefFDuyg6su/L/bqlUrfffdd7rvvvscNklI554W8MEHH6h///4OX58xY4amTp1qd1B+fuSVK6+8skiThCSFhobq+uuv12effeZwmzx69KiWLVvm6lsr4tixY3YnK2JiYjRp0iR98cUXGjJkiJo3b664uDiFh4erfv36uuKKK/Tqq69qwYIFxX4Pfffdd1q6dGmZa3NVamqqHnroIeXm5hZ5rXXr1lq0aJGef/55JSQkODx5JJ17Gsujjz6qb7/9Vg0bNizy+rp16/TBBx+YqufLL78sMi8kJETvvvuuXn31VXXq1KnEJgnp3JMZunfvrmeeeUarVq3S+++/ry5dushisZiqAQAAAADc6a+//tKMGTOKzP/nP/9Z4k0LSUlJeuutt4rMf/DBB/Xss886vFnjvAEDBujDDz8sMnLh1q1bvXLBOSEhQatWrdLDDz+shIQEU430FotF3bt31+eff65WrVrZvTZz5kwPVeoZ//vf//Tjjz/azQsLC9P777+vO+64o9jj7fDwcI0bN07/+c9/irw2e/bsUj385fxF83r16umzzz4r9mYN6dzTUydPnqzLL7/cbv6ZM2cc3ogEAAAAAOWttMfcEsfdHHdz3L1p0yZ99tlndvOCgoI0adIkPfTQQ8U+gNJqtWr48OGaMmVKkXuGli5dWmHPGWRnZys0NFTTpk3TiBEjiv38LBaLBg8ebLvx/0Lz5s0r9bqlc/+nPvjgA4dNEufVqFFD7777rq699lq7+YZhaMKECXbNGwAA82iUAOATCg/jdqGkpCRTOd58803Nnj1b3bp1M3Vw6ci1116rqVOnOvxRPHv27FLl9KQ+ffpo2bJlGjZsmCpVqlSqHA0aNNCMGTPUrl27Iq/973//08mTJ8taZqmEhobqzjvvLPb1EydO6JlnntHll1+u+++/X19++aX2798vwzDKrcZ58+bpnXfeUceOHUu1fFBQkIYMGaKXX365yI3iBQUF+uijj9xRpk2rVq00Z84c1ahRw2msxWLRM88843D4xG3btum9996zTY8dO1YPPfRQsScxLrtYHaoAAMcsSURBVFSnTh09//zzDl+bP3++0+WduXDkhcjISM2YMUMDBgxwulyTJk300Ucf6eKLL3b4+jPPPKOzZ8+WuT5XvPTSSw5H37j88ss1d+7cEoe2LKxBgwaaO3euw079qVOnKj09vcTlk5OTtXv37iLzhw8frl69epmu40IWi0U9e/bUrFmzGEoRAAAAQLn7888/NXz48CIP6IiIiNA999xT4rIff/xxkab2vn37auTIkabW3aZNGz377LNF5s+ZM0f5+fmmcrhLRESE06b34sTGxurFF1+0m7d58+Yiozz6sg8//LDIvEcffVRdu3Y1tfwtt9yiW2+91W6eYRilvnElJCREb7zxhurUqeM01mKxFHlKnyStXr26VOsGAAAAAHcpyzG3xHH3eRx3V9zj7lmzZhWZN3ToUN1www2mlu/evbvuv//+IvMd/XtUFOPGjTP9/+6uu+4qcl9NWf/dx48fX+zDOy8UFBSkF154ociDMA8cOKAffvihTDUAQEVFowQAn1Dck+2lc0+aN8NdQ3VdfPHFuvvuu4vMX7FihcOblr2pRo0aCg0NLXOeqKgovfjii0U6ynNycvTVV1+VOX9pDRkyRI0aNSoxJj09XUuWLNGTTz6pq666Spdcconuuusuvf7661q2bJmOHTvmsfrc9X/ummuucXhA++WXX9rd+F8W4eH/j737jmvq+v8H/gqEvUFwgFsBRXEUQVzUVeuetVp368fdqh1qq7bVutpqrava1lVX1bZqXXVvC4qIoyqoKAooe4MYSPL7w5/5erkJJCEQhNfz8fDx8L5zzrlvkpDkhvM+xxJLly4VrZxRFHNzc41fVL38AqlNmzYYN26cTrm0bdtWbXHJpUuXBNs/ltT333+v1YXmSxYWFli7dq3aL0WSkpJE22qWpqioKLXnq1evHlasWKHX772TkxN+/PFHUUFLVlZWsUUqjx8/VhvX9osYIiIiIiIiY3v+/DkSEhJw5swZzJ07F3369FFbEP7NN9+gWrVqRY7z559/CmJmZmaYM2eOTvn06NED/v7+gtiTJ09w8uRJncYxNk9PT/j4+AhiYWFhRspGN9euXcOtW7cEMU9PTwwbNkyncT755BPRrqhHjx5FcnKyzjn16tULTZo00bp9gwYNRPd/4Z+JiIiIiIiotBnqmvvlWLzu/j+87q58190JCQk4ceKEIObi4qK28KEoY8aMEU22Dw8Pr5TfG9SsWVOn552ZmRl69OghiMXHx+u90GyTJk10mlthbm6OWbNmieI7d+7U6/xERJUdCyWIqFxwdHQUTdJ/yRg7Grz//vuiichyuRxXr14t81zKSt26ddGtWzdR/MqVK0bI5gUbGxv8/PPPcHJy0rpPRkYGLly4gHXr1mHy5Mno0KEDOnfujJkzZ+Kvv/5CRkZGKWasvwkTJohiWVlZiIiIMMj4Q4YMQf369XXu17Vr1yJXuPj8889Fu2Foo3v37qJYfn4+7t27p/NY6nTq1AldunTRuZ+dnR1mzpyp9rbCW1uWJk2rmcyePVunYpfCmjZtqva+L273Ek2vw0UVuREREREREZW1y5cvw8vLS+0/X19fdOjQAePHj8fu3bvx7NkzQV8rKyssXboUvXv3LvIcN2/eRGZmpiDWqVMnrXZvLOy9994TxS5evKjzOMZWu3ZtwfG1a9eMk4iO1N3XQ4YM0XmnWnt7e/Ts2VMQy8/Px+XLl3XO6Z133tG5T9OmTQXH0dHROo9BRERERERUnLK45gZ43a0Or7sr13V3cHCwaK5A3759YW1trdM4ZmZman/e1/F3oKQGDhyo8/NO3aKcDx8+1Ov877zzjs7zaoKCgkSFZcHBwaLddoiIqHj67e1FRFQKbGxskJ6eLorn5eUZJZdWrVqJLhCuXbumtpigonjzzTdx8OBBQez69etGyuaF2rVr448//sDUqVP1rmyPjY1FbGws9u3bh6+//hodO3bE0KFDtd5WryzUqVMHderUEV1UX79+XacVDdSRSCQYPny4Xn0dHBxQr1493L17V3Sbv78/PD099Rq3RYsWauN37twRrcigj2nTpundt1u3bvDx8RE93+7evYtr166hefPmJUuuGDKZTPR7CLy4z9q1a1fi8YcNGyYaPzo6GvHx8RpXcNFULJOSkoLq1auXOCciIiIiIiJjsbGxQY8ePTB58mStrm/ULaLx9ttv63Xuzp07w9zcXLC7YnlYpOPx48e4du0aIiMjERMTg+zsbGRnZ0Mmk0GpVIraP336tMjj8krdff3WW2/pNVb37t2xY8cO0fiFVx8siqWlpU47Y75Uq1YtwbFcLkdOTg5sbGx0HouIiIiIiMiQdL3mBnjdzetu9SrTdbchfwe6d++O77//vtjxK7pWrVrp3KdmzZqiWFZWll7n12eRTxMTE3Tu3Bnbt29XxZ4/f447d+7o9TwmIqrMWChBROWGpaWl2rgxCiWAFxc7hQslKvoWdIVXIgCA1NRUPHnyxKgrx9esWRM7d+7E5s2bsWHDBrUFNdqSyWQ4evQojh49irZt22LGjBnw9vY2XLIlUKtWLVGhxH///VficevVq6f2Ik5b9evXV1soERQUVKIxJRKJ6IuepKQkvcd8ydvbG15eXiUao0+fPmp/3y9dulTqhRJXrlxRe4Gty5c8RfH19YW1tTVyc3MF8bCwMNFKHC9VqVJFbfyvv/7ClClTDJIXERERERGRMTRp0gTDhw/XesKGumtFfRc4MDc3h5eXF27evKmK3b9/HzKZTLTTaWlTKBT4888/sXv3bkE++ii88md5dfv2bcFxtWrV4OrqqtdYPj4+MDExgUKhUMV0/R6xRo0aGnfcLYqdnZ0olp2dzUIJIiIiIiIyOl2vuQFed2uD190vVNTr7sL3m1Qq1XtOi7u7O1xcXJCSkqJx/MqgTp06OvdR97jrUyhRtWpVjfMtitO4cWNR7NatWyyUICLSkW57ChERlaJXL2hepev2Y4bi5OQkir168VARqfuZASA5ObmMMxEzNzfHuHHjcPLkScycORONGjUq8ZgXL17EoEGD8Mcffxggw5Irreecpt0btOXs7Gzwca2trWFlZSWK61uB/ypD7PqiaYyy2GHl0qVLauPt27c3yPhSqVRtIUl4eLjGPj4+Pmq/LFy7di12795tkLyIiIiIiIhKytraGt7e3qJ/DRo00PiH+EuXLmHQoEFqd/ZTJy0tTXBsZmYmWllQFw0aNBAcKxQKZGRk6D2ePqKiojBw4EDMnTu3xJM1gBeTBco7pVIpup/r16+v93g2NjaiiT+FnyvFcXBw0Ovc6naBzM/P12ssIiIiIiIiTcrimhvgdbc2eN39QkW97i78c3l4eMDCwkLv8Qrf77rebxWBPo+9use9oKBA53Hq1q2rc5+X6tWrJ4pV9HlrRESlgTtKEFG58ep2h6/StNOENrKzs/Hvv/8iIiICd+/exaNHj5CdnY2cnBzk5OTo/CH2dajMVyqVuHr1Km7evIm7d+/i/v37SEtLU/3M+uzQYYjJ64Zia2uL999/H++//z6ioqJw9uxZhIaG4urVq3rtNJGfn485c+bg6dOn+Oijj0qcX0pKCoKDg3H37l3VVpkv7/vc3FzI5XKdxjPEc87Dw6NE/TWthGCIcQvvamCIL3V8fHxKPEb16tXh7OyM1NRUQfzGjRslHrs46lZwMDc3L9EXgIWpK8qJj4/X2N7S0hJvvvkmjh07JogXFBRg7ty5+P333zF06FB06dJFY2ENERERERFRaWvSpAm2bt2q8fb09HScPn0av/zyCx48eKCK5+fnY8aMGbC2tkanTp2KPEfh63R1q8vpwt7eXhTLyMjQe4VFXd29exejRo0SXf+WhD5/NC5r2dnZou9oDPFYxsXFqY51nXijz6qWREREREREZaUsrrkBXndrg9fdL1TU6+7S/h2QyWR49uyZ2oUtKypjPvYlefwMtasFEVFlx0IJIioXlEolcnJy1N6mT6HEuXPnsHv3bpw9e1ZjAYY+yvMHzidPnmDr1q04fPhwkROe9VFeC0Tq16+P+vXr4/3334dSqUR0dDRu3bqFW7du4fbt27hx44ZoIr4ma9asQYMGDdCjRw+d81AqlTh8+DD27NmD4OBgnYshimKI55y6L390oWnb0dIY1xC/r4bYbeTlOBcvXhTEkpKSkJubC2tra4OcQ51Hjx6JYlKpFLNnzzbYOe7duyeKFfdF0uTJk3Hq1Cm1X7zdvn0bc+fOxddffw1fX18EBATgjTfeQPPmzUv8PCEiIiIiIjIUR0dH9O/fHz179sSsWbNw6NAh1W1yuRyfffYZ/v777yIXBij8/VVJ/6isrr+m78gMLT8/H9OmTVM7WaNly5Zo27YtmjRpgmrVqsHNzQ0WFhawtLSEqampoO2sWbOwd+/eMsnZUNTdxyW91i/8WJbV40hERERERFQeGOKaG+B1N6+7Nass191l9TtQmQoljKkk97MxX7+IiCoSFkoQUbmQlpamcWu6KlWqaD3O48ePsWDBApw9e9ZQqQk8e/asVMYtCZlMhvXr1+OXX34ptfz02YWirEkkEtStWxd169ZFr169ALz4wuX27ds4efIkDh8+rHYC+qvmzp2LNm3awNHRUevz3r59G/PmzcO1a9dKkL1mhnhMS+sCtzxeOJuYmMDNzc0gY2kaJysrq1QLJRISEkSx3NzcUv/yq7gdWby9vfHFF19g/vz5GtvI5XKEh4cjPDwcwIvfy4YNGyIgIACtW7dG27Zty+XzhoiIiIiIKhdzc3N89913SE5OxqVLl1Tx7OxszJ49G7/99pvGvoV3XSzpdbu6/pp2djS03bt3IyoqShCrVasWfvjhBzRt2lTrcV6H740KU3cfa7vYhiaFH8uyehyJiIiIiIjKk5JccwO87laH190vVJbrbhsbG8Fipq/z7wCV7PHjY0dEZBgmxk6AiAiAYHu8wqpVq6bVGLdu3cI777xTakUS5dHz588xefJkrFixolSLOJRKZamNXZpMTU3RtGlTTJs2DUePHsWKFStQp04dje2zs7OxadMmrcc/d+4chgwZUmpFEqQ7Q14U2traqo3ruoWnLvLz8432RZc2Kw8MGzYM3333ndbFDkqlEnfv3sXWrVsxefJktGnTBp999pmqkIKIiIiIiMhYpFIpvvvuO9G1X0hICA4fPqyxX+Fd80q6E6S6/g4ODiUaU1sHDx4UHNvY2GDTpk06TdYASvc6ubTY2tqKVugs6WNZeEfWsnociYiIiIiIyht9r7kBXnerw+vuFyrLdbehfwcK32/m5uZc3LAMleTxU9fXzs6uJOloRdNCx0REryvuKEFE5UJkZKTG22rWrFls/wcPHmD06NGiD/iFmZmZoUaNGnB1dYWdnR1sbGxgamoKExNx3didO3cQERFRfPJGNGXKFJw7d67Ydq6urqhatSqcnJxgY2MDMzMzSKXit4Dc3FwcPXq0NFI1OolEgrfffhvt27fHZ599hpMnT6ptt3v3bkybNg0SiaTI8UJDQzF58mTIZLIi21laWqJGjRqoUqUK7OzsYGVlBalUqnb8sLAwPH78WPsfikQMWSihaazs7GyDnaOw58+fl9rYhtK3b18EBARg1apV+Pvvv3W6SM7NzcX+/fuxf/9+dOjQAXPnzkWtWrVKMVsiIiIiIiLNqlWrho8++giLFi0SxH/44Qd07doVZmZmoj7Ozs6C4/z8fDx+/Fjva5v79+8Ljk1MTMrkD/05OTmihR/69esHDw8PnceKiYkxUFZlRyKRwNHRESkpKapY4VU+dZGbm4unT58KYk5OTnqPR0RERERE9LrT55ob4HW3OrzurlzX3c7OzoiNjVUdx8bGQiaTwdzcXK/xCt/vFfV+K6+io6P17vvw4UNRzMXFRWN7dfPACgoKdD5venq6zn2IiMozFkoQUblw/fp1tXEPDw+tLlLnzZunsUjC19cXvXv3RkBAABo0aCCqWtdk1apV5bpQYt++fRqLJJydndGvXz906NABTZs21bgyfmGxsbEVtlDiJRsbG/z4448YMmQIbt26Jbo9NTUVkZGR8Pb21jhGfn4+vvzyS41FEoGBgejZsyf8/PxQp06dYosuXpo1axYLJUrIkIUGmh5fCwsLg52jMG1fn4ytWrVqWLhwIT777DMcOnQIZ86cwZUrV3TaLvXcuXPo378/li1bhjfffLP0kiUiIiIiIirC0KFDsXXrVsGkg5iYGPz5558YOnSoqH3jxo3xzz//CGL//fefXhM2ZDKZaPGQhg0b6v2Hb10kJiZCoVAIYn5+fjqPk5KS8lpO2ABePJbnz59XHcfHxyM5ORlVqlTReaxbt26J7k8fH58S50hERERERPQ60/WaG+B1d2G87n6hMl13N27cGDdu3FAdFxQU4M6dO2jWrJnOYz158kRQrAJU3PutvIqPj0dKSkqRBQ6aqJvTVNTjp25umD4LgT569EjnPkRE5RkLJYioXLh48aLauDZbDp4+fRohISGiuJWVFebNm4e+ffvqlVNOTo5e/cpCQUEBli5dqva2Xr164ZtvvoG1tbXO45bnn9mQzM3NMWPGDIwaNUrt7WFhYUUWSuzatQsPHjwQxZ2cnLBs2TK0bdtWr7wqy/1fmgy524OmsQpvdWlIlpaWkEgkUCqVgnhgYCA2b95caufVl6OjI4YNG4Zhw4ahoKAAt27dwpUrV3DlyhVcvXq12JUGsrOzMXXqVGzduhW+vr5lkzQREREREdErzM3NMXnyZMyaNUsQX7duHQYOHCiaPNGyZUvRGEeOHEGPHj10Pvfp06dFBf8tWrQoso+6AvvCEwW0kZaWJorps6Lm4cOHde7zkqmpKeRyuer41f+XhRYtWggmbADA0aNHMWzYMJ3HOnLkiCim7rlCRERERERUmeh6zQ3wurswXne/UJmuu1u0aIGdO3cKYkeOHNGrUELd/Vbc7wAZ3okTJ/Duu+/q1EehUODkyZOCmIWFBRo1aqSxj7q5LDExMWjSpIlO5w4NDdWp/UuGev0kIjI0E2MnQER09epVxMXFqb2tffv2xfYvvJrAS99++63eRRIAkJGRoXff0hYaGoqkpCRRvGPHjli2bJleRRJA+f6ZDS0gIEDjSgXJyclF9lX3nJNKpVi3bp3eRRIAt68zhPz8fJ12NSiKpt+H0iyUkEgkap+Xr26tWV5JpVI0a9YMH3zwAdauXYuQkBDs378fH3/8cZGrGuTl5WHOnDmi4hAiIiIiIqKy0qdPH9SpU0cQi4+Px+7du0VtfX19RdeFp0+fRkJCgs7n3bFjhyjWrl27IvvY2NiIYvpcB6v77kjdJI6i5OfnY9u2bTqf+6XCP4uhrue1pe57x127dun8B9ysrCwcPHhQEDMzM0NAQECJ8iMiIiIiIqoIdLnmBnjd/Sped79Q2a67AwMDRRPO9+/fr/PjV1BQoPb3rLjfATK8P/74Q+c+586dQ3x8vCAWGBgIMzMzjX3q1q0rir26O4k25HI5/vrrL536vGSo108iIkNjoQQRGV3hSuiXTE1N0bFjxyL7KpVKnD17VhT38/NDt27dSpSXPhfaZeX06dNq41988UWJxi3PP7OhSSQSjbtGFPUFRXp6OsLDw0Xxnj17onnz5iXKKTExsUT96YV79+4ZZJy7d++KYhYWFrCzszPI+Jp4eHiIYk+fPoVMJivV8xqaRCKBl5cXxo8fjz179uDPP/9Eq1at1LaNjIzEuXPnyjhDIiIiIiKiF0xNTTFp0iRR/OeffxatPGlubo5BgwYJYjKZDIsWLdLpnEeOHBHtkOru7o5OnToV2U/d6pMxMTE6nRsAXF1dRTFNO75qsnr1akRHR+t87pcKT3zR5+coCV9fX9GKepGRkfj99991Gmf58uWixS/efvttuLi4lDRFIiIiIiKi154u19wAr7tfxevuFyrbdXfVqlXRpUsXQSw5ORmrV6/WaZzffvsNDx8+FMRatmyJxo0blzhH0s3Nmzexb98+rdvn5+fj22+/FcWHDBlSZD8XFxdUq1ZNEDty5IhOu8ls375d79cKQ71+EhEZGgsliMiooqKiRJXfL3Xs2BHOzs5F9k9LS1O7Cn/Xrl1LlJdCocD169dLNEZpUncx7OXlhVq1apVoXHUFABWZo6Oj2nhRKxg8fvxY7UVESZ9zqampJfqSg/7PnTt3SjzGs2fP8OjRI1G8cePGMDEp3Y9P6nZfKCgowOXLl0v1vKWtadOm+O2330Rf6rxUeNtVIiIiIiKistS7d2/Uq1dPEEtMTFT7x/vhw4eLVm87cuQI1q9fr9W5bt++jTlz5ojiI0eOVLtF/asaNmwoiqlbRKQ4Li4uohU9Dxw4gIiICK36//XXX/jll190Pu+rPD09BcehoaFlvsrcmDFjRLHvvvtO62vwP//8U7RCqUQiwejRow2RHhERERERUYWgyzU3wOtugNfdL1XW6251P9+mTZtw6NAhrfqfP38ey5cvF8Xff//9kqZGevrmm29w69atYtsplUrMnj0bDx48EMRr166NoKCgYvt36NBBcPzkyRP89ttvWuUYHByM77//Xqu26hjq9ZOIyNBYKEFERqNQKDBnzhyNlasjR44sdozk5GS1cXUrsuviv//+Q3Z2donGAKB2y7OCgoISj6vu5y7pzwzgtZ+IrauMjAy1cVtbW419UlJS1MZLev9funSpRP3p/+i6EoemMdQVzDRr1qzEYxfnjTfeUBvXtJPM68TU1BRffvml2mITbb8UJCIiIiIiKg0mJiaYMmWKKP7rr7/i2bNngpi7uzumTp0qavv9999j3rx5RX6ntG/fPowePRpZWVmCuK+vL4YPH15sno6OjqLJJXv27MHmzZtFYxane/fuguP8/HyMHTu2yO8oMjMzsWDBAsyePVt13VzU9yhFadGiheA4KysL06dPR1RUlF7j6aNXr16iPzLn5eVh/Pjx2LZtm8bFNJ4/f45ly5Zh7ty5UCqVgttGjRolWjGTiIiIiIioMtPlmhvgdTevu3nd3bJlSwwdOlQQUygUmDFjBlatWgWZTKa2n1wux6ZNmzB58mTk5+cLbuvatWuJFwAl3VlYWAAAsrOzMWbMGPz9998a2yYmJmLy5MmiNhKJBPPnz9dqUc933nlHFFu6dCl27twp+l166fnz5/jll1/wv//9DzKZTJWzrgz5+klEZEhSYydARJXXt99+i6tXr6q9rV27dggICCh2DE0XwPp+aHtJ163+NLGxsRHF8vLySjxuTk6OKGZpaVmiMUNDQ3Hv3r0SjfG6UbdjAPDiixdNyvtzjoAzZ84gMzNTtJWoLvbv3682XvjLpNLQoUMHmJubi77c+Pvvv/HRRx+p3a7wdVK1alXUq1cP9+/fF8RTU1ONlBEREREREdEL3bt3x9q1awXfjyQnJ2PHjh344IMPBG0/+OAD/Pvvv/j3338F8R07duDAgQPo1KkTmjZtChcXF+Tk5ODRo0c4fvy42t0k7ezssHTpUkil2n1dP3DgQMHqbnK5HIsXL8aSJUtQrVo12NnZif5wOmTIENEf2EePHo3t27cjMzNTFUtKSsLIkSPRqlUrtGvXDu7u7jAxMUFSUhLCw8Nx7tw5weqTgYGBqFq1Kvbt26dV7q/q168fVqxYIVhU5MyZMzhz5gwcHBzg4uICc3NzQR83Nzf8+uuvOp+rKIsXL0bfvn2RlJSkiuXm5uKbb77Bhg0b0LVrV9SvXx92dnZIS0tDREQEjh8/jrS0NNFYPj4++OSTTwyaHxERERERUUWgyzU3wOvul3jdLVSZrrtnzZqFsLAw3L17VxUrKCjA6tWr8fvvv6NLly7w8vKCk5MTMjMzcf/+fRw/fhzx8fGisapXr44FCxaUZfr0/3Xt2hX3799HREQEMjIyMGPGDKxbtw5du3ZFrVq1YG1tjaSkJISFheHs2bNq55QNHz4crVu31up8vr6+6Ny5M06ePKmKyeVyfPXVV9i+fbvqvObm5khNTcWtW7dw9uxZwaKxX3zxBb766iu9fl5DvX4SERkSCyWIqMwplUosW7YMmzdvVnu7ubk5vvjiC63G0jRhWN0Hf209efIEBw8e1Lv/q9QVSiQkJJR4XHUTwEvyMwMo8baNr5uIiAg8fvxY7W1FrT6gafJ9QkKCqDJaWzdu3OCOEgYkk8nw+++/Y/z48Xr1j4mJUbt7g729vVZbGZaUra0tunXrhgMHDgjiWVlZWL9+fYX44kfda6O6HXiIiIiIiIjKkomJCSZPnoxp06YJ4uvXr8fQoUNhbW0taPvTTz9h+vTpomvIrKws/P3330WuEPfSywkItWvX1jrPYcOGYd++faIFL5RKJZ4+fYqnT5+K+qjbndTR0RFLly7FxIkTRTu+hoaGIjQ0tMg8PD09sWLFCixevFjr3F/l5uaGiRMnYtWqVaLbMjIy1O4EWhqrz7m4uOD333/H2LFjRRNqnjx5gt9++02rcd544w2sXbtWNMmEiIiIiIiIdLvmftme19287n5VZbvutrS0xG+//Ybx48fjxo0bgttSUlKwa9curcapX78+1q9fD0dHx1LIkopjZmaG1atXY8iQIarXiQcPHuDnn3/Wqn+fPn20nkP30rx583Dz5k0kJiYK4nfv3hUU3qjzwQcfYMiQIXoXShjq9ZOIyJCK34+HiMiAnj59inHjxhVZgT5nzhzUr19fq/FcXFzUxs+fP69XfgqFAjNnztS4TZ2uPDw8RLGoqCjRFne6qlKliih248YNwUoEuvjzzz9x7ty5EuVkaH///TfCwsJKbfxly5apjTs6OsLHx0djP1dXV7Vxfe+/Z8+eYebMmXr1Jc3WrVund1HSokWL1L4G9O3bF1ZWViVNTSvvv/8+JBKJKL5x40bRqimvG4VCgZiYGFHczc3NCNkQEREREREJvf322/Dy8hLEUlNTsW3bNlFbKysrrFmzBhMnTtRrp8/27dtj165d8Pb21qmflZUVNmzYgLZt2+p8zsKCgoKwYsUK2Nra6tSvY8eO2L59e4l3PZw0aRKmT59u9OL5mjVr4vfff0f37t3VXo8XxczMDCNGjMCmTZte+10giYiIiIiISpMu19wAr7t53f1CZb7udnZ2xpYtWzBkyBCtd0R5SSKRoFevXvj9999Ro0aNUsqQtFGzZk1s375d67lwACCVSvG///0PS5YsEe3AUBxXV1fs2LFDpwIxMzMzzJw5EzNmzNDpXIUZ8vWTiMhQWChBRGXi6dOn+Pbbb9G9e/ciJ5SPGDEC7777rtbjOjo6ql3F/8SJE4iIiNApR6VSiUWLFuHy5cs69StK48aNRbGcnBycOXOmROO2aNFCFMvPz8fatWt1Huvy5ctYuHBhifIpDdevX8d7772HUaNGGfQxAYBVq1ZpfB727du3yAtMT09Ptavh79q1S1SNXRyZTIYZM2bgwYMHOvWj4uXm5mLq1Kl49uyZTv02b96MU6dOieImJiYYMmSIodIrVuPGjdGnTx9RvKCgAFOnTsW1a9cMdq7IyEgcP368yDZ//PEHQkJCDHK+I0eOIDU1VRRX93pJRERERERU1iQSCT788ENRfMOGDcjOzhbFTU1NMW3aNBw7dgwjRoyAu7t7keNbW1ujc+fO+O2337B+/Xq9/1BdtWpVbNy4EXv27MG4cePQrl07uLu7w97eXuc/nHft2hX79+/Hu+++W+TEExMTE/j7++Onn37CunXrNO66qQsTExNMmDAB586dw5dffokePXrA09MTTk5OsLCwKPH4unB2dsaPP/6IP/74Az169Ch28oWrqysGDx6Mw4cPY86cOWWeLxERERER0etG12tugNfdJcXr7teflZUV5s2bhwMHDmDgwIEaF/d8ydHREb169cKePXuwbNmySldcUl7VqVMH+/btw2effVbk65KFhQXeeust/PXXX/j0009hamqq1/lq1qyJ/fv34+OPPy7yOWNmZoYePXpg3759eP/99/U6V2GGfP0kIjIEiVKpVBo7CSIqPwpX778UGRmp0zjPnj3DvXv3cPnyZZw9exZXrlyBQqEoss+oUaN03i4MAJYsWYJNmzaJ4u7u7vjtt99Qs2bNYsfIzs7G119/jQMHDhTbVpf7IiIiAn379hXFq1SpgoULF6JDhw46V/6+zEHdJGqJRIIFCxZg0KBBWo2zf/9+zJkzB8+fPy+y3eLFizFgwACd8yyJ+fPnY/v27arjRo0aoW/fvujVq1exF36aJCcn49tvv8X+/fvV3m5lZYVjx44Vu7L9hx9+iGPHjoniPj4+2LBhA5ycnIrNJSkpCTNnzsTFixeLbOfu7q524r46sbGx6Ny5syhe0sdv1apVWL16tSiu6+tCYZ06dUJcXJwg1r9/fyxZskSr/rNmzcLevXuLbNOmTRssW7YMzs7ORbZTKpX47bffsGTJEqj7aKTr65O63HR5LAEgLS0Nffv2VbszhpmZGT7++GOMGDFCr9U/ZDIZLl68iG3btuHChQt49913MX/+fI3tX/48Pj4+GDlyJLp06aLzqicAEBYWhokTJ6rdxvXgwYNo2LChzmMSERERERGVNw8ePMD9+/eRmpqK9PR0WFlZwdnZGdWrV4evry/Mzc2NnaJGMpkM169fx8OHD5Geng6FQgE7OzvUqlULTZs2haOjo7FTLDNyuRz//fcf4uLikJqaiuzsbNjZ2cHFxQV16tTReUVSIiIiIiIiMgxed1cMvO7Wj1KpRGRkJKKjo5GSkoKsrCzY2trC2dkZHh4eaNKkiV7zkMgwCs+D0TQH5u7du4iIiEBiYiIKCgrg4uKCqlWromXLlnrNxShOREQEIiIikJ6ejmfPnsHOzg5169ZF8+bN1S4WS0RUkbBEi4i0MmvWLI23KZVKyGQy5OXlITU1FYmJiXj69KnaycbqWFlZ4euvv0a/fv30ym348OHYtm0b8vPzBfG4uDj069cPkydPxqBBg9RW2yckJODIkSP4+eefkZKSoopLpVK0adOmyN0vtOHt7Q0fHx/cunVLEE9OTsb48ePh7OysqtbXtHKAug/MXl5eaNu2rWiSvVKpxOzZsxEcHIxx48apLXyRyWQIDg7Gr7/+itDQUMFtnTp10mkid1m6c+cO7ty5g++//x6tW7dGhw4d0KpVKzRs2LDILzpkMhmuXbuGf/75B3///TdycnI0tp0xY0axRRIAMGbMGLWFErdu3ULv3r0xdepU9O7dW+1j+vjxYxw4cAAbNmwQ5GJra4vGjRsbfPeMysbV1RW2trZ4+PAhAODff/9Fjx498OGHH6JHjx6iIha5XI6QkBCsW7dO433v4eGBadOmlXbqIk5OTli5ciVGjx4t2hkjPz8f3377LX777TcMHz4cHTt2RP369TVuU6pQKPDw4UPcunULZ8+exZkzZzSuylKUW7duYebMmTA3N0f79u3RqVMnNG3aFA0aNNC4koFSqcTNmzexa9cu7Nu3DwUFBaI2Xbp0YZEEERERERFVGPXq1VO7A+rrwNzcHK1atUKrVq2MnYrRmZqaolmzZmjWrJmxUyEiIiIiIqJX8Lq7YuB1t34kEgm8vb1ZSPKa8/T0hKenZ5mdj88ZIqrMWChBRFopbtV2fXXr1g2zZs3Se7tD4MUk5hEjRmDjxo2i27Kzs/Htt99i2bJl8PLyQo0aNWBhYYH09HQkJCTg/v37ags6pk6diufPn5e4UAIApkyZgokTJ6q9LTU1FSEhIUX217S6/qefforLly+LCkSAFyuzHzx4ENWqVYOXlxfs7Ozw7NkzpKSk4O7du8jNzRX1ady4MT799NNyWyjxklwux8WLF1VFImZmZvDw8ECNGjVgb28Pc3Nz5OXlIScnB/Hx8YiOjlY7Mbuw4cOH47333tMqh5YtW6Jbt244evSo6LakpCTMmTMH8+fPR+PGjVG1alWYmZkhNTUVT548QXR0tKiPRCLB/Pnzcf78ea3OT5qZm5tjyZIlGD58uOp3Iy0tDfPnz8fixYvh6emJqlWrwtLSEomJiYiKikJaWprG8SwsLPD999/D2tq6rH4EgebNm2PVqlX48MMPRcUSABAfH4+lS5di6dKlcHJyQp06deDo6AhbW1vk5eUhKysLmZmZePjwodr++pLJZDh58iROnjwJALC0tETdunXh6OgIe3t7WFhY4NmzZ0hKSsKDBw+QmZmpcSwnJyfMmzfPYLkRERERERERERERERERERERERERsVCCiMqcmZkZunXrhvfffx8+Pj4GGfOTTz5BREQE/v33X7W3FxQU4NatW6KdHdQZMWIExo0bh1WrVhkkt06dOmHChAlYt26dQcZ7qXHjxpg/fz4+//xzjW3i4+MRHx9f7Fh16tTBr7/+iry8PEOmWCby8/Px8OFD1Q4CupJKpZg0aRImT56sU79Fixbh4cOHuHv3rtrbX+5koY2ZM2eiZ8+eLJQwkObNm2PhwoWYNWsWFAqFKp6fn6/16wDwouhi+fLlaNmyZWmlqpX27dtjy5YtmDZtmmCLxsLS0tKKLPooTXl5ebhz547O/apWrYoNGzagSpUqpZAVERERERERERERERERERERERERVVYmxk6AiCoHR0dHdO7cGYsXL8bFixexbNkygxVJAC8mu69atQrdunXTewwzMzPMnDkTc+bMMVheL02fPh2rVq1CrVq1DDrugAEDsHDhQlhYWOg9Rps2bbBr165yN1G5R48eePvtt2FjY1Nq5/D398e+fft0LpIAAFtbW2zcuLFEW2JaW1tj6dKlGDNmjN5jkHp9+/bFihUr9N4JomrVqti0aRM6d+5s4Mz04+vri7///hvvvvsuTE1NDTp27dq1ERgYWGQbMzMzg54TAN58803s2rULDRs2NPjYREREREREREREREREREREREREVLlxRwkiKjEzMzOYm5vD0tISjo6OcHFxgZubG2rXro26deuiUaNGqF+/PiQSSanmYWtri5UrV+KPP/7A2rVri1x5/VUmJiZ488038cknn6BBgwallt9bb72Ft956C1euXMGFCxcQGRmJBw8eICsrCzk5OXrv5jBo0CA0adIEy5Ytw7lz57TuV6dOHUycOBH9+vXT67ylzc/PD35+fpDJZAgJCcH58+cRFhaGyMhIFBQU6D2unZ0dunbtij59+hQ7Obw4rq6u2Lx5MzZt2oRNmzYhJSVFq35mZmbo0aMHpk+fjurVq5coB9Lsrbfego+PD7799lscP35csLuEJhYWFhg8eDCmTJkCR0fH0k9SB3Z2dpg/fz5GjRqFLVu24ODBg8jOztZrrDp16qBdu3bo2bOnVjtmzJs3D/3798fZs2dx7tw5REREaHV/FmZqaorWrVtj1KhRCAoK0id1IiIiIiIiIiIiIiIiIiIiIiIiomJJlEql0thJEBEZWkFBAc6cOYOQkBBcvXoVSUlJyMjIgEKhgI2NDdzc3FCvXj20atUKHTt2hLu7u2iMzMxMZGZmiuIeHh5l8SPo5e7duzh16hRCQ0MRHR2NjIwM5ObmwsLCAk5OTqhTpw6aNm2K9u3b44033hAVrxQUFCA+Pl40rpOTU6nu7KCL3Nxc3LhxA3fv3sWjR48QHR2NJ0+eIDs7W1VwYmFhARsbG9jY2MDJyQn16tWDt7c3vL290bx5c5ibmxs8r7y8PJw8eRIhISG4fv06UlNTkZ6eDolEAhsbG9SoUQP169eHv78/OnXqBBcXF9EYqampyM3NFcSkUimqVatm8HwrglmzZmHv3r2CmLu7O06dOiVqGxMTg+PHj+Py5cuIiopCSkqK6rni5uaGhg0bok2bNujevTucnJzK6kcoEZlMhsuXL+PKlSu4c+cOYmJikJycjNzcXCgUClhbW8PGxgb29vaoU6cO6tatiwYNGsDf37/Ez6ns7GzcvHkTN27cQHR0NB4/foynT58iJycHOTk5AAAbGxvY2tqiWrVq8Pb2RqNGjRAUFARXV1dD/PhERERERERERERERERERERE5UanTp0EC/v2798fS5YsMWJGRETEQgkiIiJ6LelSKEFEREREREREREREREREREREVFpYKEFEVP6YGDsBIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiQ5EaOwEiIiIiIiIiIip9MpkMGRkZyM3NhVwuh1wuN3ZKREREREREejE1NYWJiQksLCxgb28Pa2trSCQSY6dFRERERERERETlCAsliIiIiIiIiIgqKKVSiYyMDFWBBBERERERUUVQUFAAAHj27BnS09NhamoKOzs7ODk5wdLS0sjZERERERFRZXTq1Cljp0BERIWwUIKIiIiIiIiIqIJKTk5GcnKyKC6RSGBqamqEjIiIiIiIiEpOLpdDqVQKjtPT05GZmYlatWrBysrKiNkREREREREREVF5wEIJIiIiIiIiIqIKKC0tTVAkYW5uDgcHBzg4OEAqlUIikRgxOyIiIiIiopJRKBTIzs5GZmYmsrOzoVQqoVAo8PjxYxZLEBERERERERERCyWIiIiIiIiIiCqa7OxsxMfHq47d3Nzg7OzM4ggiIiIiIqowTExMYG9vD3t7e8jlcsTGxiI3N1dVLFG7dm1YWloaO00iIiIiIiIiIjISE2MnQEREREREREREhpWamqr6v7OzM1xcXFgkQUREREREFZapqSlq1qwJa2trAC92m0hLSzNyVkREREREREREZEwslCAiIiIiIiIiqkAUCgVyc3MBAFKpFG5ubkbOiIiIiIiIqPSZmJjAw8NDVSSelZUFpVJp5KyIiIiIiIiIiMhYWChBRERERERERFSB5OTkqCYD2dracicJIiIiIiKqNExNTWFrawsAkMvlqiJyIiIiIiIiIiKqfFgoQURERERERERUgWRlZan+b2dnZ8RMiIiIiIiIyp69vb3q/5mZmUbMhIiIiIiIiIiIjImFEkREREREREREFYhMJlP939ra2oiZEBERERERlT0bGxvV/1+9PiIiIiIiIiIiospFolQqlcZOgoiIiIiIiIiIDCMqKgoymQwmJibw8vIydjpERERERERlLiIiAkqlEhYWFqhXr56x0yEiIiIiIiIiIiPgjhJERERERERERBWIQqEAAJiY8GsfIiIiIiKqnExNTQEAcrncyJkQEREREREREZGx8C/mRERERERERERERERERERERERERERERERUYbBQgoiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIKgwWShARERERERERERERERERERERERERERERUYXBQgkiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIqowWChBREREREREREREREREREREREREREREREQVBgsliIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiowmChBBERERERERERERERERERERERERERERERVRgslCAiIiIiIiIiIiIiIiIiIiIiIiIiIiIiogqDhRJERERERERERERERERERERERERERERERFRhSI2dABERERERERERERERERGp5+XlJTjesmULAgICjJSNZrGxsejcubMgdvLkSXh4eBgpIyIiIiIiIiIiIqrMuKMEERERERERERERERERERERERERERERERFVGNxRgoiIiIiIiIiIiPSyZ88efP7556K4VCrFP//8g1q1auk17ttvv42HDx+qjvv3748lS5bonWd5dOnSJYwcOVKrtqamprCzs4OtrS1q1KgBHx8ftGjRAh07doS5uXkpZ0pEVPmoe38zxC4OI0aMwOXLl1XH7u7uOHXqVInGpJLr1KkT4uLiim0nkUhgZWUFOzs7ODs7w9vbGz4+PujatSuqVatWBpkSERERERERERGRLrijBBERERERERERERlUQUEBVq5caew0Kgy5XI709HTExsbi8uXL2LRpEz766CO0b98ey5cvx/Pnz42dIhERUYWnVCqRm5uLhIQE3LlzB3v37sWCBQvQqVMnjB8/HlFRUcZOkYiIiIiIiIiIiF7BHSWIiIiIiIiIiIjI4A4dOoRx48bB09PT2KlUWOnp6Vi3bh2OHj2Kn376CfXq1TN2SkRERJWOXC7HmTNn8O+//2L27NkYMmSIsVMiIqJClEolnj9/jszMTOTm5kKhUEAulxs7LSIiIiIiIoMyNTWFiYkJrK2tYW9vDwsLC0gkEmOnZVQslCAiIiIiIiIiIiKDUygUWLFiBdasWWPsVF4bTZo0gbu7uyheUFCArKwsPHjwAMnJyaLbHz58iLFjx2Lnzp1wc3Mri1SJiKgMRUZGGjuFSsXJyQn+/v6iuFKpRE5ODhISEvDgwQMoFArB7TKZDF9//TVsbGzQu3fvskqXiIiKoFQqkZycjIyMDOTn5xs7HSIiIiIiolJVUFAAAHj27BlSUlJgZmYGBwcHVKlSpdIWTLBQgoiIiIiIiIiIiErFiRMncOPGDfj6+ho7ldfCsGHDMGDAgCLbRERE4Ndff8XBgwcF8bi4OCxevBjLly8vzRSJiIgqvIYNG2LlypVFtsnIyMCePXuwevVqZGdnq+JKpRJffvkl2rZtC2dn59JOlYiIiqBUKhEXF4esrCzRbRKJBKampkbIioiIiIiIqPTI5XIolUrVcX5+PpKTk/H8+XO4u7tXymIJFkoQERERERERERGRwZiZmQlW6ly+fDk2bdpkxIwqFm9vbyxbtgzNmjXDwoULBbf9888/mDBhAry8vIyUHRERUeXg4OCAMWPGoFOnThg6dChSUlJUt+Xm5uLXX3/FzJkzjZghEVHlpq5IwsbGBnZ2drCzs4NUyqkyRERERERUMb3cpTwrKws5OTkAgKysLMTFxVXKYgkTYydAREREREREREREFUfhHRH+/fdfXLp0yUjZVFwjR45Ely5dBDGlUonjx48bKSMiIqLKp3bt2pg/f74ofuzYMSNkQ0RELyUnJ6uKJCQSCWrWrIlatWrBycmJRRJERERERFShSaVSODk5oVatWqhZs6aqMCIrKwvJyclGzq7ssVCCiIiIiIiIiIiIDOb999+Ho6OjILZ8+XLjJFPBjRw5UhRjUQoREVHZ6tKlC9zd3QWx2NhYxMbGGikjIqLKTalUIiMjQ3Xs4eEBW1tbI2ZERERERERkHLa2tvDw8FAdZ2RkQKlUGjGjssdSeSIiIiIiIiIiIjIYGxsbjBs3Dt99950qFh4ejtOnT6Njx45GySkhIQH//fcfUlNTkZaWBktLS7i4uMDDwwNNmjSBqampUfIqqRYtWkAqlaKgoEAVi4uLK9GY2dnZuHHjBhITE5GWloaCggI4OzvDzc0NLVq0MPgEo6ioKERGRiIxMRHPnz+Hg4MD6tevj2bNmsHc3Nyg5yIiquwUCgWuX7+OR48eISkpCQBQpUoVeHl5oVGjRqrV5UpbamoqwsPDERMTg7y8PNjb28PNzQ1+fn6iYsvXhZ+fn+g9OC4uTvCHaF0olUrcvn0bjx8/RmpqKrKysmBvbw9nZ2f4+PigZs2ahkhbJTMzE1evXkV8fDzS0tJgZWUFDw8P+Pr6ws3NzaDnIiIqbc+fP0d+fj6AF9enLJIgIiIiIqLKzNbWFtbW1sjNzUV+fj6eP38OS0tLY6dVZlgoQURERERERERERAY1bNgwbN68GYmJiarYjz/+iDfffLPMJmHKZDLs2LED+/btw507dzS2c3R0RIcOHTBhwgTUr1+/THIzFHNzczg4OCAlJUUVS0tL03kchUKBAwcO4K+//sLVq1dVk4oKMzMzQ8uWLfHBBx8gKChI77yVSiV2796NrVu34t69e2rb2NjYoH///pg0aRJcXFwAALNmzcLevXtVbfr3748lS5bonQcR0evCy8tLcLxlyxYEBARo3T8nJwdr167Fvn37VAUShVWvXh0jRozAyJEjYWZmBgDo1KmTYPL/4sWLMWDAAD1+ghdu3ryJ1atX49y5c1AoFKLbTUxMEBgYiE8++QQ+Pj56n8cYXF1dRTF93pPv3r2LDRs24Ny5c0hNTdXYrnbt2ujXrx9Gjx4Na2trnc/z0p07d7BixQpcuHBB7fu/RCKBv78/pkyZAn9/fwAvdq8qvKtVZGSk3jkQERlaZmam6v92dnZGzISIiIiIiKh8sLe3R25uLoAX10yVqVDCxNgJEBERERERERERUcViaWmJiRMnCmIRERE4fPhwmZz/33//Rffu3bF48eIiiyQAID09Hfv370efPn3wzTffQCaTlUmOhlJ4i2RNRQ6aXL16Ff369cOMGTNw6dKlIvvn5+fj0qVLGDduHEaPHl3kBE5NEhISMHToUHz55ZcaiySAF5N6t23bhh49euDSpUs6n4eIiF4IDQ1Fz5498euvv2oskgCAp0+f4rvvvsOgQYMQHx9v8DxWr16Nd999F2fOnFFbJAG8KNy7ePEiBg0ahB07dhg8h9JU+P0YgGDHp+JkZWXh888/R9++fbFv375i32MfPXqEFStWoGvXrjh79qxe+S5fvhyDBg3C6dOnNb7/K5VKVWHEt99+q/bnJCIqb15O/gFYKEFERERERAQIr42ePXtmxEzKHgsliIiIiIiIiIiIyODeeecd1KxZUxBbuXIl5HJ5qZ73wIEDGDduHGJjY3XqV1BQgG3btmH8+PHIyckppewM6/nz58jIyBDEHB0dte5/6NAhjBo1Sq9VoIODgzF48GA8evRI6z4JCQkYOXIkwsPDte6Tnp6OsWPH4urVqzrnSERU2V26dAn/+9//8PTpU637REREYNiwYXoVw2nyzTffYNWqVVp/BlAoFJg3bx72799vsBxK26u7aL3k4OCgVd+nT5/ivffew549ezQWkWiSnJyMiRMnYvfu3Tr1++abb7Bu3TqtizmUSiU2btyIBQsW6HQeIiJjePlaKpFIIJVKjZwNERERERGR8UmlUtWO76X9d7ryhleFRERERERERESkUb5cAQVXji1zJhIJzExf7zVOzMzMMGXKFMycOVMVi46Oxp49e/DOO++UyjkvXLiAzz77TLTasaurK3r16gV/f3+4uroiNzcXDx8+xD///IOQkBBB23///RdTp07Fr7/+qvrSuLy6evWq6Avt+vXra9X30KFD+OSTT0T3lbe3Nzp37owmTZrA2dkZUqkUKSkpuHbtGg4cOICYmBhV25iYGEyaNAl//vknrKysijxfQUEBJkyYgOjoaEFcKpXirbfewptvvgl3d3eYm5sjISEB//77Lw4fPoz09HTIZDJMnToVzZo10+pnI9JGgaIACqV2E5IlEgnMTMxKfE6lUol8hfa7vpiZmBnkdShfka/1KvAmEhNITUr+pyOFUoEChXgCtqHGp+LFxcVhwoQJohXiHB0d0bNnT7Rp0wZubm6QyWSIjY3FmTNncPz4cRQUFCA2NhZffPGFQfLYsWMHtm3bpjpu3bo1unTpggYNGsDBwQEZGRm4ceMGdu/eLSpyXLhwIdq1awdnZ2eD5FJalEolrly5Ioo3aNCg2L6pqakYOnSoqJjF3t4e3bt3R8uWLeHu7g4bGxtkZWXhwYMHOHXqFM6dO6dqK5fL8dVXX6FmzZoIDAws9pybNm3C9u3bRfHGjRujV69e8Pb2hqOjI9LS0nDnzh0cOnRItUPXtm3buKsEEZV7L6+RTE1NjZwJERERERFR+WFqaoqCggIWShARERERERERUeWWmZeP4KgUPEl/hvRc7Sd0kmE5WpuhhqMVAuu7wN6y5BN0jaFPnz749ddfcf/+fVXsp59+Qt++fWFubm7Qc6Wnp+Pzzz8XTd7r378/5syZA1tbW0E8ICAAQ4YMwalTp/D5558jPT1dddv58+exbds2jBgxwqA5Gtpvv/0mirVp06bYftHR0Zg7d67gvqpWrRrmz5+PoKAgtX2CgoIwefJkbNy4ET/++KPqi/T79+9jyZIlmDdvXpHnXL9+PW7fvi2I1a9fHz/88AO8vb1F7bt27YoPP/wQs2fPxqlTp5CYmIiTJ08W+7MRaSs8MRyh8aFatW3k3Agda3Us8TnzFflYf3O91u3HNh0Lc9OSv1ZeiL2AO6l3tGrbqlortKrWqsTnTMtLw67IXaU2PhXvyy+/RG5uriDWuXNnLFiwQFR44Ofnh379+iEiIgLTp0/HgwcPcPr0aZiYlLxoc+PGjQBeFC0uW7YMAQEBojaBgYEYPXo0PvvsMxw9elQVT09Px+7duzFhwoQS51Gajh49Kip0qFOnDqpXr15kP6VSiZkzZwr6mpqaYuzYsZg4caLaIsSAgAAMHToU165dw/Tp0/HkyRMAL1ZP/+yzz3D48GHY29trPOejR4/w448/CmJSqRSzZ8/G0KFDRcVZ7dq1w9ixY7Fjxw4sWrQIBQUF+P3334v8uYiIiIiIiIiIiMoLFkoQEREREREREZHK/cRsHL0VD1mBdqtsU+lJz81Hem4+7idmo5tPNTRwsy2+UzljYmKCqVOn4sMPP1TFnjx5gt9//x2jRo0y6Ll+/vlnJCYmCmIDBw7EokWLiuzXqVMn/PLLLxg9erRgQukPP/yA/v37iwosyotNmzbh9OnTgpiFhQX69etXbN85c+YgJydHdVynTh1s3boVbm5uRfaTSqUYN24cHB0dMXfuXFX8zz//xIQJEzROCM3MzMTatWsFMXd3d2zevLnIczo7O2PVqlWYNGkSzp49C4WCr0tEZDyrVq1Suwq/Lu7du2egbIp24cIFXLhwQRALCgrCypUrIZVq/tOgt7c3Nm/ejKFDhyIuLs4gr7tKpRLOzs7YuXMnPDw8NLazsLDA0qVLERkZKdh9aO/eveW6UOLBgwf4+uuvRfF333232L579uwR7AxhamqKpUuXokePHsX2bd68OXbs2IFBgwYhOTkZAJCUlISdO3di3LhxGvutXLkSeXl5gtiSJUvQu3dvjX0kEgmGDRsGe3t7fPrpp3w/JiIiIiIiIiKi1wYLJYiIiIiIiIiICMCLnSRYJFH+yAoUOHorHm72tV/LnSXeeustNGnSBP/9958q9vPPP+Odd96BtbW1Qc7x7Nkz/PXXX4KYh4cHvvrqK636N2vWDJMnT8b333+viuXm5uKvv/4yeEFHSUVEROCXX37BoUOHRLf973//Q9WqVYvsf+3aNYSG/t8q+lKpFCtWrCi2SOJVgwcPxvHjx1WTOwsKCrB161bMmDFDbft9+/aJJmV+9dVXWp1TKpViwYIF6N69O7Kzs7XOkYjI0F597Szvdu7cKTi2tbXFwoULiyySeKlq1ar46quvipxsr6tvvvmmyCKJl8zNzTF27FjMmTNHFYuOjkZKSgpcXFwMlo8hZGRk4K+//sLq1asFxYfAiwLE4cOHF9lfqVRiw4YNgtioUaO0KpJ4qXr16pg9ezamT5+uim3ZsgUffPABTE1NRe1TU1MFO3YAQPfu3YssknhV7969cfz4cdEYRERERERERERE5RULJYiIiIiIiIiICAAQHJXCIolySlagQHBUCrr5VDN2KnqZNm0axo4dqzpOSUnBli1bDLZC9PHjx5GRkSGIffjhh7CwsNB6jFGjRuG3334T7Erx559/lmmhxPbt23HmzBlRXC6XIzMzEw8ePFCtGl1Yt27dMHny5GLPsWXLFsFx9+7d4e3trXOuI0eOFKyCff78eY2FEn///bfg2NfXF0FBQVqfy83NDe+++65oQikREYllZWWJdhwaMmQIXF1dtR4jKCgITZs2xc2bN0ucj6enJ7p06aJ1+86dOwsKJQDg9u3baN++fYlz0da9e/fw0Ucfqb0tJycHCQkJiIqKUruzgqurK9auXQtzc/Miz3HhwgVERUWpji0tLbV6Hy+se/fuWLx4serzS1JSEiIiIuDj4yNq+88//yA/P18QmzJlik7n++ijj1goQURERERERERErw0WShAREREREREREQDgSfozY6dARXidH5/27dvD398fly9fVsU2bNiA9957D/b29iUe/8qVK4Jja2trdOvWTacxzMzM0KdPH6xfv14Vu3fvHjIzMw2Sozb+++8/wc4b2rC2tsb48eMxbtw4mJiYFNlWqVTi4sWLgljPnj11zhMAWrVqBRMTE9Uk0Xv37iEjIwMODg6Cdnl5eYiIiBDEtF25+lV9+vRhoQQRkRZu3LiBgoICQUyf193evXsbpFBC1/djZ2dnuLm5CQoX4+PjS5yHLtLS0vQqBggKCsL8+fNRrVrxha3nz58X9bW1tdX5nBKJBP7+/jh48KAqFhoaqrZQ4urVq4Ljxo0bo0GDBjqdr0GDBmjUqBHu3Lmjc65ERERERERERERljYUSREREREREREQEhUKJjGf5xTcko8l4lg+FQgkTE4mxU9HLtGnT8N5776mOMzMzsX79enz88cclHvvatWuC45YtW8LKykrncdq2bSsolFAqlbh+/XqZrmKti+7du+PLL7+Es7OzVu3v3r2L9PR0Qaxx48Z6ndvS0hKOjo5ITU0F8OK+io6ORrNmzQTtbt26JZqw27JlS53P5+XlBRsbG+Tk5OiVLxFRSW3ZsgUBAQElGmPEiBGCosHScOPGDcGxra0tvLy8dB5Hn9dqdZo3b65znypVqggKJbKzsw2SS2mpXr06li5dCj8/P637FC7y1Pf9GACqVq0qOH748KHadoULX/R9jFu2bMlCCSIiIiIiIiIiei2wUIKIiIiIiIiIiGBiIoGDlRnSc1ksUV45WJm9tkUSAPDGG28gKCgIZ8+eVcW2bt2KUaNGwcXFpURjP3r0SHCs72RDdf0ePXpUbgsl/vnnH1hZWWHevHkwNzcvtr26SY0dOnQwWD6FizAAIDY2VnBsYmICT09PnceWSCTw9PREeHi4vukREVUKhV93GzZsCIlE988Pnp6ekEgkUCqVJcqnSpUqOvexsbERHJf3IrmnT59i9uzZWLVqlVbvcUqlEpGRkYLY8uXLsXz5coPko+79GADi4uIEx/oU0JSkHxERERERERERUVljoQQREREREREREQEAajhasVCiHKvhqPsOCeXNtGnTcO7cOdWky9zcXKxduxZz5szRe0yZTIa8vDxBzM3NTa+xHB0dYWlpKRgvIyND79x0tXjxYgwYMEAQUygUyMnJwcOHD3H+/Hls374dKSkpqtv37NmDpKQkrFu3DlJp0V/3apo4aSjq7qusrCzBsbW1tVZFHeo4Ojrq1Y+IqDIp/Lrr5OSk1zgWFhawtrYucZFC4aIHbRQu7ChpsYau/P39sXXrVlH82bNnSExMRHh4OHbv3o2wsDDVbdHR0Rg+fDi2bt1abCFBZmamaLclQ8rMzBTFcnJyROfU97mhbz8iIiIiIiIiIqKyZmLsBIiIiIiIiIiIqHwIrO8Ccym/LiqPzKUmaFO/ZLsulAeNGzdGt27dBLFdu3bhyZMneo+pbnK+ra2t3uPZ2dkVO35ZMjExgZ2dHXx9fTF58mQcOXIErVu3FrQ5f/48li1bVuxYpV0ooW7SZ+EJu/pMmH2pJI8rEVFlUd5ed/XZzaK8srKyQu3atdGvXz/s2LEDX3zxheDny8jIwOTJk0WPQWGl/dlC3ftxdna2KKbvc4Pvx0RERERERERE9LrgjhJERERERERERAQAsLc0Qzefajh6Kx6yAoWx06H/z1xqgm4+1WBnaWbsVAxi6tSpOH78OORyOYAXO0KsWbMGCxcuNNg5SjIps/Cq1eVtgqe9vT3Wrl2Ld999F3fv3lXFN23ahE6dOqFVq1Ya++bnC3eMsbCwwJtvvmmw3Nzd3UWxwrtHFM5BFzKZTO++RESVBV93y86oUaOQlZWFVatWqWIxMTFYsmRJkZ9r1D0mfn5+cHExTFFsgwYNRDEzM/HnSH2fG3xeEBERERERkbEV3s1xy5YtCAgIMFI2msXGxqJz586C2MmTJ+Hh4WGkjIgqHxZKEBERERERERGRSgM3W7jZ10ZwVAqepD9Deq7+k+uoZBytzVDD0QqB9V1gX0GKJACgXr166Nu3L/bs2aOK7d27F2PHjkXdunV1Hs/BwUEUK24l56IUXnHZ3t5e77FKi7W1Nb7//nsMHDhQtWq0UqnE/PnzsW/fPpiamqrtV/i+UiqVWLFiRakWgxS+/9StaK2tnJyckqZDRFThFd4ZqSSvuyXpW1lMmjQJ586dw/Xr11Wxv/76C4MHD0azZs3U9lH32WXIkCHo3bt3qeWp7vOMvo8vnxdERERERERERPS6YKEEEREREREREREJvNxZAgDy5QooCq2wT6XPRCKBmamJsdMoNVOmTMGBAwdUKxnL5XKsXLkSy5cv13ksc3NzWFpaIi8vTxVLTEzUK6+MjAzBOID6yYzlgbe3N4YOHYqtW7eqYnfv3sW+ffswcOBAtX2cnJwExzKZDImJiahatWqp5anunAkJCXqd8/Hjx4ZKiwgt3Fqgmav6ScyFGaqYyMzEDGObjtWpvSG082iHtu5ttWprIjHMe4+TpZPan9VQ45NmhV93Y2Nj9RonPj6+RLtRVBYmJib46quvMHDgQNWuVEqlEt9//z22bdumto+jo6MoFhcXV5ppQiqVws7OTlBMGhMTo9dYfD8mIiL6P3v27MHnn38uiJXXFa1J/eOliZmZGWxtbWFnZ4datWqhcePG8PPzQ7t27TQuUEFEVJGV1nveiBEjcPnyZdWxu7s7Tp06VaIxqeQ6deqk1XcVEokEVlZWsLOzg7OzM7y9veHj44OuXbuiWrVqZZApUfH4jTQREREREREREWlkZmoCC6kp/5Xxv4pcJAG8+GPHu+++K4j9888/iIiI0Gu82rVrC45v376t1zjq+hUeuzyZOHEirK2tBbGffvpJtctEYXXq1BHFwsLCSiM1lcJboAP6PT7Z2dl6T+gkUkdqIoW5qblW/wxVsCCRSLQ+p7mpuUELNLQ9p9TEMOtrmUhMSnV80szb21tw/OjRI712AND3vbQy8vHxQbdu3QSx0NBQBAcHq20vlUrh4eEhiF25cqXU8nup8HND38f4zp07hkiHiIiIqFzLz89HWloaHj9+jAsXLuCXX37BuHHj0LFjR2zcuBFyudzYKRIRERmdUqlEbm4uEhIScOfOHezduxcLFixAp06dMH78eERFRRk7RSIWShAREREREREREVHZmzhxIqysrFTHSqUSP/74o15jNW/eXHAcHh4u2hlCGxcvXhQcSyQSNGum3YrzxuDi4oKhQ4cKYrGxsdi3b5/a9s2aNRMVVpT26lw1a9aEi4uLIHby5Emdxzl58qRqpW4iItKs8PuWUqnU67Ven9fqymzy5Mmi4qbVq1drbB8YGCg4vnz5sl4FLbrw9fUVHF+4cEHnz0t5eXmiz0tERERElUlCQgK+/fZbDB06FMnJycZOh4iIqFySy+U4c+YM+vXrh507dxo7HarkWChBREREREREREREZa5KlSoYMWKEIHb69GmEh4frPJafn5/gOCcnB8eOHdNpjIKCAuzfv18Q8/T0hL29vc75lKX3338flpaWgti6devU7iphbm4Of39/QezIkSNabaFdEu3atROdMyMjQ6cxdu3aZciUiIgqrPr168Pd3V0Q0/U1ND09HUeOHDFkWhWep6cnunTpIohduXIFISEhatsXfm98/vw5tm3bVmr5AUD79u0Fxzk5OTh48KBOYxw4cAA5OTmGTIuIiIjIqFq1aoVu3bqJ/nXq1AlvvPEGHBwc1Pa7fv06xo0bx89GRERGEhkZKfgXEBBg7JQqNCcnJ7Xvl2+99Rbatm2LBg0awMREPB1dJpPh66+/xoEDB4yQNdELLJQgIiIiIiIiIiIioxg7dizs7OwEsR9++EHncbp27Sr6w/WqVasgk8m0HmPr1q1ISEgQxAYNGqRzLmWtSpUqeOeddwSxmJgYjX94GDlypOA4Pz8fCxYsKNXdGgYPHiw4zsrKwrJly7Tuf/DgQYSFhRk6LSKiCkkikYjev65cuaLThPgffvih1Hc3qIgmTpwoiq1Zs0Zt286dO4sKWjZs2IDHjx+XSm4A0Lp1a9SuXVsQW758OTIzM7Xqn5mZqffuX0RERETl1YcffoiVK1eK/q1duxY7duzApUuXsGPHDlGhKwDcunULa9euNULWREREZathw4Zq3y9XrVqFjRs34tChQwgJCcGsWbNga2sr6KtUKvHll18iNTXVSNlTZcdCCSIiIiIiIiIiIjIKBwcHvP/++4LY5cuXERsbq9M4VlZWGDhwoCD2+PFjLFiwQKv+N2/exMqVKwUxGxsbDBgwQKc8jOV///sfzMzMBLG1a9dCLpeL2rZt2xYtWrQQxE6dOoXvvvtO72KJmzdv4saNGxpv9/PzQ9OmTQWxXbt2YfPmzcWOHRYWhrlz5+qVFwDExsbCy8tL8K/wTiZERBXNO++8AxsbG0Fs7ty5uHr1arF9N2/ezF189OTj44OgoCBB7PLlywgNDRW1NTMzw4QJEwSxzMxMTJo0SVS4qa309HQcPnxY4+0SiQSjRo0SxJKTkzFhwgTk5uYWOXZubi7Gjx+P5ORkvXIDgBEjRojek3X9zEdERERU1iQSCd544w2sX78eY8aMEd2+detWTvwkIiLCi7/3jBkzBnv27IGLi4vgttzcXPz6669GyowqOxZKEBERERERERERkdGMGjVK9KV5fn6+zuOMHz8ebm5ugtiuXbswZ84c5OTkaOx39uxZjB07VjRB8OOPPxatfFReVa1aVVTU8ejRI427SixYsEA0gXbjxo0YN24cHjx4oNU5c3NzcejQIYwePRqDBg1CZGRkke0XLFggKuZYvHgxpk+fjqioKFH75ORkrFixAqNGjVI9NjVr1tQqNyKiys7V1RWffPKJIJabm4uRI0di5cqVSElJEfWJiorCtGnTsHjxYgAv/rhdeLcmKt6kSZNEsdWrV6ttO3DgQNHKxPfu3cOAAQNw5MgRKBQKrc55/fp1LFiwAB07dsTWrVuLbDt06FA0b95cEAsLC0Pfvn1x4sQJ0Wew/Px8nDhxAn379lUV2tSqVUurvIiIiIgqEolEgpkzZ8LHx0cQz8vLw7lz54yUFRERUflTu3ZtzJ8/XxQ/duyYEbIhAqTGToCIiIiIiIiIiIgqLxsbG4wbN041MVNfjo6OWLx4McaOHSvYGeGPP/7AmTNn0KdPH7Rq1Qqurq7Izc3Fw4cPceTIEfz777+isdq3b4/hw4eXKJ+yNm7cOPz1118oKChQxdauXYs+ffrAxES4Xk6DBg3w/fffY8qUKYJJmOfOncOFCxfQpk0btG7dGp6ennBycoKpqSkyMzORlpaGyMhI3Lp1C6GhocjLy9M6P29vb3zyySdYsmSJIH748GEcPnwYderUgbu7O6RSKRITExEZGSnIbfDgwcjPz0dMTIwqJpFItD4/EVFl89577+HixYs4efKkKpafn481a9Zg3bp18PT0hJubG/Lz8xEbG4vHjx8L+s+dOxfLly9HRkaGKsbX3eI1b94cgYGBCA4OVsVCQkIQFhaGN954Q9DW1NQUP/zwAwYPHozo6GhVPDk5GVOnTkWdOnXw5ptvonnz5nB1dYWNjQ1ycnKQlZWF6Oho3Lp1C2FhYXjy5InW+ZmYmGDx4sV47733kJaWpoo/fvwYkydPhoODAxo0aAAHBwekp6fj/v37yMzMVLWrWrUqPv74Y0ybNk33O4eIiIjoNSeRSDB8+HB8/vnngnhISAj69etnnKSIiIjKoS5dusDd3R1xcXGqWGxsLGJjY+Hh4WHEzKgyYqEEERERERERERERGdV7772HzZs34+nTpyUap127dvj+++/x+eefC1ZETkpKwoYNG7Bhw4Zix2jTpg1WrFhRojyMwcPDA3369MGePXtUsejoaBw6dAi9e/cWte/cuTPWrVuHTz/9VDABUqFQ4MKFC7hw4YLBcxwzZgyeP3+O5cuXi26Ljo4WTBJ9VVBQEObOnYs5c+YI4paWlgbPkYioopBIJPjxxx/x0Ucf4fTp04Lb5HI57ty5gzt37qjtO23aNPTu3RvLli0TxPm6q51JkyYJCiUAYM2aNdi4caOorYODA7Zv346pU6fiypUrgtuio6OxefNmg+dXr149bN68GaNHjxYUSwBARkYGwsLC1PZzdnbGzz//jPT0dEHcysrK4DkSERGRdrKzs3H9+nUkJiYiJSUFEokELi4ucHNzQ/PmzWFtbW3sFCucVq1aiWKvTgLVR3p6Oq5fv47U1FTB41itWjW0aNECFhYWJRq/sNu3byMqKgqJiYmQy+VwcnJCw4YN0bRpU5iamhr0XERE5Z1CocD169fx6NEjJCUlAQCqVKkCLy8vNGrUqMwWjUhNTUV4eDhiYmKQl5cHe3t7uLm5wc/PD46OjmWSg6H5+fmJ3iPj4uL0LpRQKpW4ffs2Hj9+jNTUVGRlZcHe3h7Ozs7w8fEx+I7UmZmZuHr1KuLj45GWlgYrKyt4eHjA19dXtLs5lW8slCAiIiIiIiIiIiKjMjc3x6RJkzB37twSj9W7d2+4uLhgzpw5Ov2h2tTUFEOGDMGsWbNgbm5e4jyMYfz48di3b59gJ4affvoJPXv2FO0qAbwoQPjzzz8xb948XLx4Ue/zNm7cGF5eXlq1nTBhAho1aoR58+YV+/iYmZlh7NixmDJlCqRSKbKysgS329nZ6Z0zEVFlYG5ujjVr1mDLli1YuXIlcnNzi2zv7OyMuXPnokePHgDA1109+fv7w8/PT1D4cPHiRYSHh6NFixai9lWqVMHmzZuxevVqbNmypdjHSRMnJyd07NhRq7be3t74+++/8c033+D48ePFtvf398eiRYtQs2ZNUXtbW1u98iUiIiL9nThxAlu3bkVYWJhgoYhXmZubw9/fHyNHjkRQUJBW4/74449Yu3at6jgwMFCrwk2FQoGAgADBQgzAi2LRLl26FNt/3759mDlzpuq4evXqOHPmjFY5lzVXV1dRrHDxqTZkMhl2796NAwcO4MaNG4Lvcl5laWmJwMBATJgwAc2bN9f5PK+eb/Pmzdi1axdiY2PVtnFycsK7776LcePGwcbGBgAwYsQIXL58WdVmypQp+PDDD/XOg4jIUAp/H71lyxYEBARo3T8nJwdr167Fvn37VAUShVWvXh0jRozAyJEjYWZmBgDo1KmT4HvtxYsXY8CAAXr8BC/cvHkTq1evxrlz59S+F5iYmCAwMBCffPIJfHx89D6PMRjqPfPu3bvYsGEDzp07h9TUVI3tateujX79+mH06NElKha9c+cOVqxYgQsXLqj9nCWRSODv748pU6bA398fAHDp0iWMHDlS0C4yMlLvHMiwWChBRERERERERERERjdgwABs2LBB464CumjTpg2OHDmCHTt2YO/evYiIiNDY1sHBAR06dMCECRPQoEGDEp/bmOrUqYMePXrg4MGDqtiDBw/wzz//oGfPnmr71K5dGxs3bsT169exZcsWBAcHIyUlpcjzSKVSNG3aFO3atUOXLl3g7e2tU55BQUE4fPgwTp06haNHjyIyMhJJSUl4/vw57O3tUb9+fbRp0wYDBgxA1apVVf0K/xGFEzOJiIpnamqKMWPGoE+fPjhy5AhOnDiBx48fi1ZJ7NixI3r37q3aHUAmkyEnJ0cwFgsltDdx4kR88MEHgtjq1as17m5lZmaG6dOnY9SoUdi6dStOnDiBe/fuQalUFnkeDw8PtG3bFh06dECHDh10KvasWrUqVq9ejZs3b+Lw4cO4dOkSEhISkJ6eDgsLC3h4eKB58+bo2bOnYLJJ4fdjPi+IiIjKTkxMDD7//HOEhoYW21Ymk6l2jGzbti0WL14suMZWp3Xr1oJCiatXr+L58+fF7mhw69YtUZEEAAQHB2tVKBESEiLKo7xS9/lMU7GKJidPnsSiRYs0Fiy8Ki8vD6dPn8bp06fRs2dPLFiwQOfJn/fu3cO0adNw//79ItulpaVh3bp1OHDgANauXav1ohhERK+b0NBQfPbZZ8XucP306VN899132L9/P37++WdUq1bNoHmsXr0aP/30E+RyucY2CoUCFy9eRHBwMObOnYv33nvPoDmUJnXvmQUFBVr3z8rKwqJFi0SLQ2ny6NEjrFixAtu3b8eiRYu0LhR9Nd8ff/wR69evLzJPpVKJS5cu4fLlyxgzZgxmzJih03mo7LFQgoiIiIiIiIiIiPQyYMCAEq2W9CqpVIqjR48aZCzgxcqJo0ePxujRo5GQkICbN28iJSVFNfnPxcVFtU2yqampwc6rrYCAgFJZUWjZsmVYtmyZzv2aNWuGZcuWQalU4t69e3j48CHS09ORnp4OiUQCGxsbODs7o169eqhbt26Jd92wtLREjx49VKuWF0ehUIjur1q1ahXbz8PDgys3EZHODPn+9qqtW7fq1c8Qr2MuLi4YNmwYhg0bplX7iIgI0R+0a9asWWQfQ73m6ns/6evUqVMGH7Ndu3Z63RfOzs6YOnUqpk6ditTUVNy8eROpqalIT09HXl4erK2tYWdnh1q1aqFevXpwdnYuca5NmzZF06ZNtW5/+/ZtwbE278dA2T+uREREFU1ERATGjh2rcdXroly8eBFDhw7F+vXrUa9ePY3tWrZsCUtLS+Tl5QEAnj9/jqtXryIwMLDI8YODg3WKF/Y6FUokJiaKYo6Ojlr337hxI7777rtiC2LVOXToEKKjo7F+/XqtPwfeu3cPI0eOLHIF7sLi4uIwcuRI7Nq1S+cciYjKu0uXLmH8+PF49uyZ1n0iIiIwbNgw/PHHHwbL45tvvsG2bdu0bq9QKDBv3jzY2tqiT58+BsujNKl7z3RwcNCq79OnTzFu3DjcvXtX5/MmJydj4sSJ+PrrrzF48GCt+33zzTfYvn271u2VSiU2btwImUyGt956S+c8qeywUIKIiIiIiIiIiIgqtKpVqxa7aiK9IJFI4OnpCU9PT2OnInDjxg3k5uYKYk2aNDFSNkREFV/hyWru7u4GmZRP2nN2dtZ59cOycOnSJcEx34+JqKKTFSggV+g+oZlKxtREAnOpibHTKDcSExMxevRo0c5OlpaWeOutt9CxY0dUr14dAPDkyROcOnUKx44dg0wmU7WNi4vDqFGjsH//fjg5Oak9j7m5OVq0aCEocAgJCSm2UKLw54OXoqKikJCQUOR3MtHR0aIVvYs7nzFdvnxZFKtfv75WfX/55Re1i1u0bNkSQUFBaNSokeqxSUpKQlhYGP7++28kJyer2t66dQvTp0/Hpk2bYGJS9O9IVlYWxo4dKyqSsLa2xttvv40OHTqgevXqUCqVePr0Kc6fP49//vkHz549Q3p6OqZNm6bacY4qh3x5PgqU2q/2ToYhlUhhZmpm7DQqhbi4OEyYMEFUJOHo6IiePXuiTZs2cHNzg0wmQ2xsLM6cOYPjx4+joKAAsbGx+OKLLwySx44dOwRFEq1bt0aXLl3QoEEDODg4ICMjAzdu3MDu3btFuw8tXLgQ7dq1K/ff0SiVSly5ckUU12ZX79TUVAwdOlT0+cDe3h7du3dHy5Yt4e7uDhsbG2RlZeHBgwc4deoUzp07p2orl8vx1VdfoWbNmlp9rti0aZPaIonGjRujV69e8Pb2hqOjI9LS0nDnzh0cOnQId+7cAQBs27ZNrwJIKjsslCAiIiIiIiIiIiKicm3Hjh2C4ypVqsDDw8NI2RARVWxyuRw7d+4UxHx9fY2UDZUnwcHBePDggSDG5wYRVUSpOTLsDY/D/cQsJGY+N3Y6lZabvQUauNmhfwt3ONuUbFfD190XX3whKpLw9fXF0qVLUbt2bUG8RYsW6NmzJ6KiovDxxx8jIiJCdVtiYiK++uorrFy5UuO5AgMDBYUSwcHBmD59usb2MpkMYWFhqmMzMzPk5+erjkNCQtC3b1+N/QvvOlG3bt1yu9iFQqFQu0tWmzZtiu175coVrFixQhBr0KABFi1ahGbNmqnt07lzZ3z00UdYtmwZtmzZooqHhIRg/fr1GDduXJHn/O677xAfHy+ItWzZEt9//73oO5UWLVqgR48emDRpEj777DOEh4fjzp07xRZj0OsvLS8NBx8cRFR6FJKfJRffgUpFFasqqO9YH73q9YKTpfpiNiq5L7/8UrQYT+fOnbFgwQJR4YGfnx/69euHiIgITJ8+HQ8ePMDp06cN8rq4ceNGAICrqyuWLVuGgIAAUZvAwECMHj0an332mWAn7vT0dOzevRsTJkwocR6l6ejRo6JChzp16qgKOzVRKpWYOXOmoK+pqSnGjh2LiRMnqi3gCwgIwNChQ3Ht2jVMnz4dT548AfDiffuzzz7D4cOHYW9vr/Gcjx49wo8//iiISaVSzJ49G0OHDoVEIhHc1q5dO4wdOxY7duzAokWLUFBQgN9//73In4uMi4USRERERERERERERFRuhYSE4MCBA4LY67K9OBHR62jt2rWIi4sTxIqa3EaVQ15eHhYsWCCIubq6ajUxkIjodXL1cRo2XHiIPJnc2KlUeomZz5GY+RxXH6dhbLu6aFGrck4cPX36NM6fPy+INW7cGJs2bYKtra3GfvXr18emTZswbNgwQaHj0aNHERISgtatW6vtVzj+33//ITs7W+O5rl27JliZe/DgwYIVmXUtlNCUl7EplUosWrQIkZGRgriLiws6d+5cZF+5XI5Zs2ahoOD/Vupv3rw5Nm7cCBsbmyL7WlpaYvbs2bC2tsa6detU8Q0bNmD48OGwtrZW2+/Bgwf4448/BDEfHx+sX7++yHPWrFkT69evx8iRI3Hr1i0oFIoi86PX2/Wk69h6eyvyCvKMnUqll/wsGcnPknE96TpGNh4JX9fXoyB91apValfh18W9e/cMlE3RLly4gAsXLghiQUFBWLlyJaRSzdOovb29sXnzZgwdOhRxcXEGeV1UKpVwdnbGzp07i1wMyMLCAkuXLkVkZCSio6NV8b1795brQokHDx7g66+/FsXffffdYvvu2bNHsDOEqakpli5dih49ehTbt3nz5tixYwcGDRqk2o0pKSkJO3fuLLK4cOXKlcjLE74OLlmyBL1799bYRyKRYNiwYbC3t8enn37K98tyjmWfRERERERERERERFTqrl27JlhlUhtXrlzBtGnTBH9okEgkGDx4sKHTIyKqcI4dO4aYmBid+mzbtg0//fSTIFa9enV06NDBkKmRESkUCuzYsQPPn2u/QnpmZiYmT56M+/fvC+IDBw6EmZmZoVMkIjKa1BwZiyTKoTyZHOsvPERqjszYqRjFtm3bBMdSqRTfffddkUUSLzk7O+Pbb78VrYSsbleEl5o0aQI7OzvVsVwux+XLlzW2DwkJERz36tULnp6eGm9/lVKpFI0dGBiosb0xKJVKhIWF4YMPPlB7v3366adqV7d+1ZEjRwSfy21tbbFy5cpiiyReNXXqVDRs2FB1nJ6ejr1792psv2vXLiiVStWxVCrFokWLtDqnra0tFi1aBFNTU63zo9dPWl4aiyTKobyCPGy5vQVpeWnFNy4HQkNDcfTo0RL9K7xjUmkpvHOmra0tFi5cWGSRxEtVq1bFV199ZdB8vvnmG612TDY3N8fYsWMFsejoaKSkpBg0H0PIyMjAxo0bMWjQINHjWqdOHQwfPrzI/kqlEhs2bBDERo0apVWRxEvVq1fH7NmzBbEtW7ZALlf/+T41NVWwYwcAdO/evcgiiVf17t0b3bp10zo/Mg7uKEFEREREREREREREpe7evXuYM2cOmjdvjl69eqFjx45q/xikVCoRGRmJbdu2Yd++fcjPzxfcPnz4cNStW7es0iYiem0dO3YM06ZNQ6dOndC9e3e0bdsWjo6OonYymQyhoaFYv349/v33X9HtX3zxBSdJVSAKhQLz5s3DmjVrVH/Qb9KkidqCh5SUFBw6dAgbNmxAfHy84Lbq1avjgw8+KKu0iYjKxN7wOBZJlFN5Mjn2hsfhg3aV61rw6dOnuHjxoiDWt29fwYT54vj6+qJbt244cuSIKnb69GmkpqbC2dlZ1N7U1BR+fn44ffq0KhYcHIxOnTqpHf/VHSFsbGzg6+uLgIAA3L17FwDw5MkTPHr0CLVr1xb1jYiIEEyklEgk8Pf31/pnMwRNq6Hn5+cjMzMT9+7dQ0ZGhtq+o0ePxoABA4o9x5YtWwTHw4cPR9WqVXXK08TEBMOHDxdM1D1//jyGDRumtv3+/fsFx127doW3t7fW5/P29kbXrl0FzxuqWA4+OMgiiXIqryAPBx8cxIjGI4ydSoWRlZUleF8DgCFDhsDV1VXrMYKCgtC0aVPcvHmzxPl4enqiS5cuWrfv3Lkz5syZI4jdvn0b7du3L3Eu2rp37x4++ugjtbfl5OQgISEBUVFRandWcHV1xdq1a2Fubl7kOS5cuICoqCjVsaWlJSZPnqxzrt27d8fixYuRmJgI4MWuEhEREfDx8RG1/eeff0R/f5gyZYpO5/voo49ExRZUvrBQgoiIiIiIiIiIiIjKzLVr13Dt2jUsWLAATk5OqFOnDhwcHAC8WHXq4cOHSE9PV9vX09MTn376aRlmS0T0epPL5Th+/DiOHz8OAHB3d4e7uztsbW0hk8mQnp6Oe/fuadxdYNCgQXjrrbfKMmUqI8nJydi0aRM2bdoEc3Nz1KtXDy4uLrC0tERWVhYSExMRHR2ttq9UKsWSJUtgb29ftkkTEZWy+4lZxk6BilAZH5+rV68KdgUAoNXE/MIGDBggmPAul8sRHh6Ozp07q20fGBgomFCqaVeI3NxcwYTRVq1aQSqVIjAwULD7QnBwsNpCicLjNmrUCE5OTtr9UAYSGhqqcx9HR0d88sknWu12mZmZiRs3bghiPXv21PmcABAQECA4fvn8KLxjSHR0NFJTUwUxbVfGLtyHhRIVV1R6VPGNyGj4+BjWjRs3UFBQIIjp+7poiEIJXXcgcHZ2hpubm2riPwDRYgalLS0tTa9igKCgIMyfPx/VqlUrtu358+dFfbXZQauwl4WXBw8eVMVCQ0PVFkpcvXpVcNy4cWM0aNBAp/M1aNAAjRo1wp07d3TOlcoGCyWIiIiIiIiIiIiIyCjS0tK03l7d398fq1atgqWlZSlnRURUccXFxSEuLk6rtiNGjMAXX3xRyhlReSCTyRAREaFVW3t7e6xcuRKtW7cu5ayIiMqWXKFEUpb6wkEqH5KynkOuUMLURFJ84woiPDxccGxpaYkWLVroPE7r1q0hlUoFk0SvXbumsVCi8Pv83bt3kZycjCpVqgjioaGhglWYAwMDAby4fjc1NYVc/mKHluDgYAwZMkR0nld3o1B33vJoxIgRmDZtmtYTN69cuSJYXdvCwgL169fX69yFJ5lmZGQgJSVF9LgULswAgJYtW+p8Pn360OtBrpAj5VmKsdOgIqQ8S4FcIYepSfne3XHLli2iIi5djRgxApcvXzZQRuoVfl20tbWFl5eXzuMY6nWxefPmOvepUqWKoFAiOzvbILmUlurVq2Pp0qXw8/PTus+VK1cEx40bN9b7/IV3bnr48KHadoULX/R9jFu2bMlCiXKMhRJEREREREREREREVOpq1aoFd3d3rSfovuTq6oqxY8fivffeK3Z7biIi+j9NmjTB2bNnkZmZqVO/xo0bY/LkyejSpUspZUbGZGJiAn9/f9GkveJIpVL07t0bkydPRs2aNUsxQyIi4zA1kcDVzgKJmSyWKK9c7SwqVZEEADx69Ehw7O3tDVNT3SfNWlhYoF69erh7967GsV/l6ekJFxcXpKT830TqkJAQ9OrVS9Cu8I4QLwsd7Ozs4OPjo5qYeunSJdHOBwUFBaLdHF6HQont27fD0tISH3/8MUxMTIptX3jS5PPnz0s08bOw9PR0UaFEbGys4NjNzU2vnTqcnZ3h6uqKpKSkEuVI5Y+piSlcrFyQ/CzZ2KmQBi5WLuW+SOJ1Uvh1sWHDhqLdeLTh6ekJiUQi2u1JV4Vft7VhY2MjOM7JySlRDqXt6dOnmD17NlatWgVPT89i2yuVSkRGRgpiy5cvx/Llyw2Sj6YdrAv/rUKfApqS9KOywUIJIiIiIiIiIiIiIip1AQEBOHXqFG7fvo3Q0FDcuHEDjx49Qnx8PHJycvD8+XNYWlrC0dERVapUQbNmzRAQEID27dvDwsLC2OkTEb12Ro8ejWHDhuHKlSsICwvD7du3ERMTg6SkJOTm5qKgoAC2trZwcHBAjRo14Ofnh8DAQJ1W+6PXj4mJCbZu3Yrk5GSEhIQgPDwc9+7dw5MnT5CWloa8vDyYmJjAwcEBjo6OaNiwIfz9/REUFIQaNWoYO30iolLVwM2OhRLlWAM3O2OnUOYKF7y6ubnpPVa1atUEhRIZGRka20okEgQEBODw4cOqWHGFEs7OzoJJgoGBgapCibS0NERGRsLb21t1+40bN5Cbm6s6lkqlRvkcqm41dLlcjqysLNy/fx+nT5/G77//rpqQqlAo8OuvvyI9PR0LFiwodnxtd9HUl7rHMSsrS3CsT5HEq31ZKFEx1Xesz0KJcqy+o347z5B6hnpdtLCwgLW1dYmLFAoXPWijcGFHSYs1dOXv74+tW7eK4s+ePUNiYiLCw8Oxe/duhIWFqW6Ljo7G8OHDsXXr1mILCTIzMwU7XxmaukVEcnJyROfU97lRkvdaKn0slCAiIiIiIiIiIiKiMtO4cWODrp5IRESamZmZITAwEIGBgcZOhcqZKlWqoFevXqIJj0RElVn/Fu64+jgNeTK5sVOhQizNTdG/hbux0yhzhSfB29ra6j2WnZ2w0KSoQgngxe4OrxZKBAcHC25PT09HRESEoP2rkzhbt26Nn3/+WdD/1UKJwuP5+vrqNXG0NJiamsLR0RF+fn7w8/PDiBEjMG7cOMFK13/88Qe8vb0xfPjwIscq7n4uKXWTSgtPCC7J/VqS5xyVb73q9cL1pOvIK8gzdipUiKXUEr3q8TrNkAz9uljSQgl9drMor6ysrFC7dm3Url0b/fr1w2+//YbFixerCjkyMjIwefJk7N27V/RZ5FXGeL/Mzs4WxfR9bvD9snxjoQQRERERERERERERERERERERVWrONuYY264u1l94yGKJcsTS3BRj29WFs425sVN5rRVeebq4SZqFC21jY2MRExODmjVrAgAuXboEhUKhsX3Lli1hbm4OmUwG4EVhxJgxY1S3v7obBfCisKK8qlatGjZs2IABAwYgMTFRFf/+++/Rrl071KlTR2Pf/Px8wbGjo6NoB4uScHZ2FsXMzYW/K4Vz0MXLx48qHidLJ4xsPBJbbm9hsUQ5Yim1xMjGI+FkydXpDYmvi2Vn1KhRyMrKwqpVq1SxmJgYLFmyBAsXLtTYT91j4ufnBxcXF4Pk1aBBA1HMzMxMqzy0wedF+cZCCSIiIiIiIiIiIiIiIiIiIiKq9FrUcsI3fW2wNzwO9xOzkJj53NgpVVpu9hZo4GaH/i3cK22RhIODg+BY3crH2irc197evsj2tWrVgru7O+Li4lSxkJAQVaFE4R0hChdKWFpaokWLFrh06RIAIDQ0FPn5+TAzM0NeXh6uXbsmaF+eCyUAwNXVFQsWLMC4ceNUsby8PCxcuBC//vqrxn6FH0NHR0esXLmy1PIExLuHGPJ5QxWLr6svZgfMxsEHBxGVHoXkZ8nGTqnSqmJVBfUd66NXvV4skigFfF0sW5MmTcK5c+dw/fp1Veyvv/7C4MGD0axZM7V9Cr9fAsCQIUPQu3fvUstT3WchfR9fPi/KNxZKEBERERERERERERERERERERHhxc4SH7SrCwCQFSggVyiL6UGGZmoigbnUxNhpGF3hCXyv7magq4SEBMGxugmJhQUEBGDPnj2q4+DgYLzzzjuq/7/k7u6uKqB4VevWrVWFErm5ubhx4wbeeOMNhIWFCVZefllUUd4FBQWhU6dOOHXqlCp27tw5hISEaCz0cHR0FBw/efIECoUCJial9/x2chJOsn7y5AnkcjlMTU11GqegoABPnz41ZGpUDjlZOmFE4xEAgHx5PgqUBUbOqPKRSqQwMxWvbE+GU/h1MTY2Vq9x4uPjS7QbRWVhYmKCr776CgMHDlTtaKVUKvH9999j27ZtavsUfr8EICjWLA1SqRR2dnbIyspSxWJiYvQa6/Hjx4ZKi0oBCyWIiIiIiIiIiIiIiIiIiIiIiArhZH0yptq1awuOIyMj9ZrwLpPJEBUVVeTY6rRu3VpQKPGy6CEhIQHR0dGqeOHdJF6Nr1ixQnUcEhKCN954AyEhIYJ2LVu2hLn567FryLRp03DmzBkoFApVbOXKlRoLJerWrSs4lslkuHnzpsYVtQ3B29tbcPz8+XNERUXB09NTp3GioqLw/Dl31alMzEzNYAZO2KeKp/Dr4qNHj5CdnQ1bW1udxrl9+7Yh06rQfHx80K1bNxw5ckQVCw0NRXBwsNrPDVKpFB4eHoIilitXrpR6nt7e3ggNDVUd6/sY37lzx1ApUSngFR0REREREREREREREREREREREVE5UniXhWfPniE8PFzncUJCQlBQIFwlvnnz5sX2KzyRMTk5GXfv3hXsJqGu3UtNmzaFjY2NIA8AWvcvj7y8vPDWW28JYmFhYaKf6SV1P9urO1KUhqZNm4p2rDh58qTO4+jTh4ioPCpcnKZUKvV6Lebrom4mT54MiUQiiK1evVpj+8LvmZcvX0Z2dnap5PaSr6+v4PjChQvIy8vTaYy8vDxcvHjRkGmRgbFQgoiIiIiIiIiIiIiIiIiIiIiIqBx54403RBMM9+3bp/M4e/fuFRybmpqKijDUcXNzQ7169QSx4OBgUVGApt0UpFIpWrVqpToODw9HQkKCaLVmTf3Lq4kTJ4oelzVr1qhtW7VqVTRs2FAQ27lzZ6lO/LSxsUHLli0FsT179iA/P1/rMfLz8wW7iRARvc7q168Pd3d3QWzXrl06jZGeni7YHYGK5+npiS5dughiV65cEe0s9VK7du0Ex8+fP8e2bdtKLT8AaN++veA4JycHBw8e1GmMAwcOICcnx5BpkYGxUIKIiIiIiIiIiIiIiIiIiIiIiKgcqVatGtq2bSuI7du3D1FRUVqPcfPmTdHEzk6dOsHZ2Vmr/oVXdw4ODsalS5dUx56enqhSpYpW/fPz87F27VrI5XJVzM7ODj4+PlrlUl54e3ujY8eOglhoaCguX76stv3IkSMFx+np6fjxxx9LKz0AwODBgwXHjx8/xsaNG7Xuv3HjRsTExBg6LSIio5BIJBg0aJAgduXKFZ0mxP/www+lvrtBRTRx4kRRTFNxYefOnUUFLRs2bMDjx49LJTfgRbFm7dq1BbHly5cjMzNTq/6ZmZml/p5OJcdCCSIiIiIiIiIiIiIiIiIiIiIionJm+PDhguP8/HzMmDFDq8ma6enpmDVrFhQKRZFjFqXwbg8XLlzA06dPNd5eXP8///xTcNyqVSuYmppqnU95oW7i5+rVq9W27d+/v2ji59atW7Flyxa9zx8cHIyHDx9qvP3tt99G9erVBbEVK1bg6NGjxY599OhRrFixQu/cLl26BC8vL8G/WbNm6T0eEZEhvPPOO7CxsRHE5s6di6tXrxbbd/PmzTrvQEEv+Pj4ICgoSBC7fPkyQkNDRW3NzMwwYcIEQSwzMxOTJk1CQkKCXudPT0/H4cOHNd4ukUgwatQoQSw5ORkTJkxAbm5ukWPn5uZi/PjxSE5O1is3ABgxYoToPTM2Nlbv8Ug9FkoQERERERERERERERERERERERGVMx07dkT79u0Fsf/++w9jx44tcsX/qKgojBkzBvfv3xfEu3XrVmxxw6sCAgJgYvJ/08vy8/MFtxfecaIwLy8vwe4VuvYvr3x9fdGuXTtB7NKlS7hy5YqorZmZGRYtWgSpVCqIL1y4EDNmzEB8fLxW50xPT8cff/yBQYMGYfTo0YKClcIsLCwwb948QUwul2PatGmYP38+njx5IuoTFxeHefPmYdq0aZDL5TAzMxMVWxARva5cXV3xySefCGK5ubkYOXIkVq5ciZSUFFGfqKgoTJs2DYsXLwYAODg4wMHBoUzyrUgmTZokimkqLhw4cKDo/fXevXsYMGAAjhw5Iir+1OT69etYsGABOnbsiK1btxbZdujQoWjevLkgFhYWhr59++LEiROizy75+fk4ceIE+vbtqyq0qVWrllZ5kXFIi29CRERERERERERERERERERERERERVm1ahW2b99eojG+/fZbWFlZqY4XLVqEPn36IC0tTRULDw9H79698fbbb6NDhw6oUaMGlEolnj59ilOnTuHo0aOQyWSCcd3c3EST54vj4OCARo0a4datW6LbTE1N4e/vX2R/iUSCgIAA/PPPP2pv16Voo7yZNGkSLly4IIitXr0amzdvFrVt3bo1vvjiC8yfP18Q//vvv3H48GEEBQUhICAA9erVU03CzcrKQnJyMu7cuYP//vsP4eHhosmaRQkKCsKIESMEE0QVCgW2b9+O7du3o2HDhqhWrRqUSiXi4+NFRTVTpkzBxYsXBQUZEolE6/MTEZU37733Hi5evIiTJ0+qYvn5+VizZg3WrVsHT09PuLm5IT8/H7GxsXj8+LGg/9y5c7F8+XJkZGSoYnxdLF7z5s0RGBiI4OBgVSwkJARhYWF44403BG1NTU3xww8/YPDgwYiOjlbFk5OTMXXqVNSpUwdvvvkmmjdvDldXV9jY2CAnJwdZWVmIjo7GrVu3EBYWprYgUBMTExMsXrwY7733nuCz1uPHjzF58mQ4ODigQYMGcHBwQHp6Ou7fv4/MzExVu6pVq+Ljjz/GtGnTdL9zqEywUIKIiIiIiIiIiIiIiIiIiIiIiKiEQkNDSzzGggULBIUSbm5u2Lx5M8aOHYukpCRV/NmzZ9i7dy/27t1b7Jg1atTAhg0b4OTkpHM+rVu3Vlso0bRpU9ja2mrVX12hRJUqVeDp6alzPuXFG2+8AX9/f1y+fFkVCw4OxtWrV9GyZUtR+2HDhql2eni1iOXlytQnTpwweI6zZ8+GTCbDrl27RLfdu3cP9+7dU9tv8ODBmDBhAs6fPy+IW1paGjxHIqKyIpFI8OOPP+Kjjz7C6dOnBbfJ5XLcuXMHd+7cUdt32rRp6N27N5YtWyaI83VRO5MmTRIUSgDAmjVrsHHjRlFbBwcHbN++HVOnThXt1BQdHa22ILGk6tWrh82bN2P06NGCYgkAyMjIQFhYmNp+zs7O+Pnnn5Geni6Iv/o5jozPpPgmREREREREREREREREREREREREZAze3t74/fffRSsva6NNmzbYuXMn6tWrp9e5Ne36oO1uEIGBgWrjAQEBeuVTnkyaNEkUW7Nmjcb2gwYNwvbt2+Hr61ui8/r5+cHDw6PYdhKJBPPnz8e3336rVZGMtbU15syZg2+++QbAi10tXmVnZ6dfwkRE5YS5uTnWrFmDWbNmwdrautj2zs7OWL58OSZOnAiAr4v68vf3h5+fnyB28eJFhIeHq21fpUoVbN68GRMmTNDqcdLEyckJHTt21Kqtt7c3/v77b3Tt2lWr9v7+/ti9ezcaNWqE7OxswW3aFJJS2eGOEkREREREREREREREREREREREROVYzZo1sWPHDpw4cQJbt25FWFgY8vPz1bY1NzdHq1atMGrUKAQFBZXovH5+fjAzMxOdS1MBRGG1a9dGjRo18OTJE0Fc20KL8iwwMBAtWrQQTPS8cOECrl27hubNm6vt4+vriz/++APnz5/Hzp07cfnyZWRmZhZ5HnNzc7zxxhto27YtunXrhlq1aumUZ79+/dC5c2ccP34cx44dw4MHD5CUlIT8/Hw4OTnB09MT7du3R//+/eHg4KDqV3hVbU4IJqKKwNTUFGPGjEGfPn1w5MgRnDhxAo8fP1bt2lSlShV4eXmhY8eO6N27t2p3AJlMhpycHMFYfF3U3sSJE/HBBx8IYqtXr8aGDRvUtjczM8P06dMxatQobN26FSdOnMC9e/egVCqLPI+Hhwfatm2LDh06oEOHDjA3N9c6x6pVq2L16tW4efMmDh8+jEuXLiEhIQHp6emwsLCAh4cHmjdvjp49ewoKPvl+Wb5JlMU9a4iIiIiIiIiI6LVx7949FBQUQCqVomHDhsZOh4iIiIiIqMzxuoio8uLvP1Um2dnZCA8PR1JSElJTUwEALi4ucHNzQ4sWLUq0AjOVHYVCgdu3byMmJgZpaWnIzMyEqakpbG1tUaVKFdSrVw+1a9eGVFq26yGnpKSgTZs2gtiff/6Jpk2blmkeRETlxY0bN/DOO+8IYsHBwXB2djZSRpVPamoqbt68idTUVKSnpyMvLw/W1taws7NDrVq1UK9ePaM8Hl9//TV+//131fGbb76Jn3/+uczzKE5lvVbijhJERERERERERERERERERERERESvEVtbW7Rv397YaVAJmZiYoEmTJmjSpImxUxEICQkRHJuZmcHLy8tI2RARGV/h10V3d3cWSZQxZ2fnEu+UVRouXbokOC5v7+mVnYmxEyAiIiIiIiIiIiIiIiIiIiIiIiKi8mHHjh2C40aNGsHc3NxI2RARGZdcLsfOnTsFMV9fXyNlQ+VJcHAwHjx4IIjxuVG+sFCCiIiIiIiIiIiIiIiIiIiIiIiIiLB3715cuXJFEOvbt6+RsiEiMr61a9ciLi5OEOPrIuXl5WHBggWCmKurK9q0aWOkjEgdFkoQERERERERERERERERERERERERVSAXLlxAZGSkTn2OHj2KefPmCWJWVlacEExEFcKxY8cQExOjU59t27bhp59+EsSqV6+ODh06GDI1MiKFQoEdO3bg+fPnWvfJzMzE5MmTcf/+fUF84MCBMDMzM3SKVAJSYydARERERERERERERERERERERERERIYTGhqKn3/+GW3atEGPHj0QFBQEV1dXUTu5XI7r169j8+bNOHbsGJRKpeD2qVOnws7OrqzSJiIqNceOHcO0adPQqVMndO/eHW3btoWjo6OonUwmQ2hoKNavX49///1XdPsXX3wBU1PTMsiYyoJCocC8efOwZs0a9O7dG926dUOTJk3UFjykpKTg0KFD2LBhA+Lj4wW3Va9eHR988EFZpU1aYqEEERERERERERERERERERERERERUQWjVCpx8eJFXLx4EQDg5uaGWrVqwc7ODnK5HBkZGbh//z5ycnLU9m/bti1Gjx5dhhkTEZUuuVyO48eP4/jx4wAAd3d3uLu7w9bWFjKZDOnp6bh3757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      "text/plain": [
       "<Figure size 4000x800 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.gridspec as gridspec\n",
    "sns.set_style(\"white\")\n",
    "fig = plt.figure(figsize=(20, 4), dpi=200)\n",
    "outer = gridspec.GridSpec(1, 3, wspace=0.2, hspace=0.2)\n",
    "alpha=0.5\n",
    "ax_fs=18\n",
    "lwd=4.\n",
    "## plot mario and iggy\n",
    "\n",
    "inner = gridspec.GridSpecFromSubplotSpec(2, 3,\n",
    "                subplot_spec=outer[0], wspace=0.1, hspace=0.25)\n",
    "ind=42\n",
    "plot_img(fig, img[ind, 0, ::].cpu().detach(),\n",
    "        0, 0, \"\")\n",
    "plot_img(fig, img[ind+1, 0, ::].cpu().detach(),\n",
    "        0, 1, \"\")\n",
    "plot_img(fig, img[ind+2, 0, ::].cpu().detach(),\n",
    "        0, 2, \"\")\n",
    "plot_img(fig, img[ind+3, 0, ::].cpu().detach(),\n",
    "        1, 0, \"\")\n",
    "plot_img(fig, img[ind+4, 0, ::].cpu().detach(),\n",
    "        1, 1, \"Data Samples\")\n",
    "plot_img(fig, img[ind+5, 0, ::].cpu().detach(),\n",
    "        1, 2, \"\")\n",
    "ax = plt.Subplot(fig, inner[1, 1])\n",
    "        \n",
    "## Plot Learned Distribution\n",
    "inner = gridspec.GridSpecFromSubplotSpec(1,1,\n",
    "                    subplot_spec=outer[1], wspace=0.1, hspace=0.1)\n",
    "ax = plt.Subplot(fig, inner[0])\n",
    "ax.plot(test_pts, low_dens.detach().cpu(), linewidth=lwd, label=\"No Reg.\",\n",
    "        alpha=alpha, linestyle=\"-\")\n",
    "ax.plot(test_pts, mid_dens.detach().cpu(), linewidth=lwd, label=\"Std. Reg.\",\n",
    "        alpha=alpha, linestyle=\"--\")\n",
    "ax.plot(test_pts, high_dens.detach().cpu(), linewidth=lwd, label=\"High Reg.\",\n",
    "        alpha=alpha, linestyle=\":\")\n",
    "ax.plot(test_pts, init_dens.detach(), linewidth=lwd, label=\"Initial\", color='gray', alpha=0.7)\n",
    "ax.legend(loc='lower left', fontsize=ax_fs-1, ncol=2,\n",
    "           bbox_to_anchor=(0.02, -0.5))\n",
    "ax.set_xlabel(\"Rotation\", fontsize=ax_fs)\n",
    "ax.set_ylabel(\"Probability\", fontsize=ax_fs)\n",
    "tick_pts = [-np.pi/2, -np.pi/4, 0, np.pi/4, np.pi/2]\n",
    "tick_labs = [r\"-$\\pi$/2\", r'-$\\pi$/4', '0', r'$\\pi$/4', r'$\\pi$/2']\n",
    "ax.set_xticks(tick_pts)\n",
    "ax.set_xticklabels(tick_labs)\n",
    "ax.set_xlim(-np.pi/2, np.pi/2)\n",
    "ax.tick_params(\"both\", labelsize=ax_fs-2)\n",
    "sns.despine(ax=ax)\n",
    "fig.add_subplot(ax)\n",
    "\n",
    "\n",
    "inner = gridspec.GridSpecFromSubplotSpec(1,1,\n",
    "                    subplot_spec=outer[2], wspace=0.1, hspace=0.1)\n",
    "ax = plt.Subplot(fig, inner[0])\n",
    "alpha=0.7\n",
    "num=4\n",
    "lwd=4\n",
    "ax.plot(angles, low_probs[:, num].detach().cpu(), linewidth=lwd, label=\"Low Reg.\",\n",
    "        alpha=alpha, linestyle=\"-\")\n",
    "ax.plot(angles, mid_probs[:, num].detach().cpu(), linewidth=lwd, label=\"Mid. Reg.\",\n",
    "        alpha=alpha, linestyle=\"-\")\n",
    "ax.plot(angles, high_probs[:, num].detach().cpu(), linewidth=lwd, label=\"High Reg.\",\n",
    "        alpha=alpha, linestyle=\"-\")\n",
    "ax.plot(angles, e2_probs[:, num].detach().cpu(), linewidth=3, label=\"E2\",\n",
    "        color='gray', alpha=0.6)\n",
    "ax.set_ylim(0.0, 1.001)\n",
    "ax.set_xlabel(\"Rotation of Input\", fontsize=ax_fs)\n",
    "ax.set_ylabel(\"Predicted Probability\", fontsize=ax_fs)\n",
    "tick_pts = [-np.pi, -np.pi/2, 0, np.pi/2, np.pi]\n",
    "tick_labs = [r\"-$\\pi$\", r'-$\\pi$/2', '0', r'$\\pi$/2', r'$\\pi$']\n",
    "ax.tick_params(\"both\", labelsize=ax_fs-2)\n",
    "ax.set_xticks(tick_pts)\n",
    "ax.set_xticklabels(tick_labs)\n",
    "\n",
    "# ax.set_yticks([0.98, 0.99, 1.])\n",
    "ax.set_xlim(-np.pi, np.pi)\n",
    "ax.legend(loc=\"lower left\", fontsize=ax_fs-1, ncol=2,\n",
    "           bbox_to_anchor=(0.02, -0.54))\n",
    "sns.despine(ax=ax)\n",
    "fig.add_subplot(ax)\n",
    "plt.savefig(\"./rotmnist_full.pdf\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
